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A Multi-Algorithmic Face Recognition System

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A Multi-Algorithmic Face Recognition System#Soumitra Kar, Swati Hiremath, Dilip G. Joshi, Vinod.K.Chadda and 1Apurva BajpaiEISD, BARC, Mumbai-400 085, Indiaskar@.inAbstractThe importance of utilising biometrics to establish personal authenticity and to detect impostors is growing in the present scenario of global security concern. Development of a biometric system for personal identification, which fulfills the requirements for access control of secured areas and other applications like identity validation for social welfare, crime detection, ATM access, computer security, etc. is felt to be the need of the day. Face recognition has been evolving as a convenient biometric mode for human authentication for more than last two decades. S everal vendors around the world claim the successful working of their face recognition systems. However, the Face Recognition Vendor Test (FRVT) conducted by the National Institute of Standards and Technology (NIST), USA, indicates that the commercial face recognition systems do not perform up to the mark under the variations ubiquitously present in a real-life situation. Availability of a largely accepted robust face recognition system has proved elusive so far. Keeping in view the importance of indigenous development of biometric systems to cater to the requirements at BARC and elsewhere in the country, the work was started on the development of a face-based biometric authentication system. In this paper, we discuss our efforts in developing a face recognition system that functions successfully under a reasonably constrained set-up for facial image acquisition. The prototype system built in our lab finds facial match by utilizing multi-algorithmic multi-biometric technique, combining gray level statistical correlation method with Principal Component Analysis (PCA) or Discrete Cosine Transform (DCT) techniques in order to boost our system performance. After automatic detection of the face in the image and its gross scale correction, its PCA and DCT signatures are extracted. Based on a comparison of the extracted signature with the set of references, the set of top five hits are selected. Exact scale of the face is ascertained w.r.t. each of these hits by first locating the eyes employing template matching technique and then finding the inter-ocular distance. After interpolating the face to the exact scale, matching scores are computed based on gray level correlation of a number of features on the face. Final identification decision is taken amongst this set of five faces, depending on the highest score. We have tested the technique on a set of 109 images belonging to 43 subjects, both male and female. The result on this image-set indicates 89% success rate of our technique.1Deputed to BARC by ECIL for this project.Index TermsFace recognition, Identification, Multi-algorithmic, Multi-biometric, Discrete Cosine Transform, Principal Component Analysis, Correlation.1.INTRODUCTIONBiometrics makes automated use of the unique personal features to establish the identity of a person. It is a tool for positive identification of a human subject as biometric signatures cannot be stolen, forgotten, lost or communicated to another, as is possible in the case of authentication employing cards, keys or passwords, so common in day-to-day use. In the present scenario of increased security concern, the necessity and relevance of making use of biometrics to establish personal identity and to detect impostors are assuming significance. Biometric techniques are based on either physiological characteristics (like finger print, iris, etc.) or behavioral traits (like voice dynamics, gait, etc.).Depending upon the suitability in a particular application, one has to choose a particular biometric [15] to be used as the basic signature for recognition. We selected the face based approach on the considerations that facial imaging, being non-intrusive, has easy client acceptance, apart from the fact that face recognition is the most natural means of biometric identification for human beings. The present circumstances around us demand increased level of security and, therefore, machine capability of personal identification from the facial image is considered invaluable. Face-based biometric systems have the potential to fulfill the requirements for access control of secured areas, surveillance, social welfare, law enforcement, etc. Although there are a few commercial face recognition systems available around the globe, their performance is not up to the mark under the practical variabilities [5]. This encouraged us to take up face recognition system development.For enhancing the accuracy of biometric systems, multi-biometric technique with the ability to utilise either multiple biometric modalities or multiple instances within a modality and/or multiple algorithms, prior to making a specific verification/identification decision, has been suggested [16]. We have developed a prototype face recognition system utilising multi-algorithmic multi-biometric technique to boost the system performance. It is based on combining Correlation with Principal Component Analysis (PCA) or Discrete Cosine Transform (DCT) technique for the purpose of deciding a facial match.The outline of the paper is as follows. In the present section, the topic biometrics is introduced and the choice of face as the underlying signature for biometric system development is justified. In section II, we make a brief literature survey on face recognition. In section III, we describe our technique of face recognition. This is followed by the presentation of performance results of our prototype system in section IV, and concluding remarks in section V.2. BACKGROUND OF THE WORKFor over two decades [4] face recognition has drawn attention of the research community. Face identification from a single image is a challenging task because of variable factors like alterations in scale, location, pose, facial expression, occlusion, lighting conditions and overall appearance of the face. With the synergy of efforts from researchers in diverse fields including computer engineering, mathematics, neuroscience and psychophysics, different frameworks have evolved for solving the problem of face recognition. Among these, the prominent approaches are those based on Principal Component Analysis (PCA), Local Feature Analysis (LFA), Template Matching, Neural Network, Model Matching, Partitioned Iterated Function System (PIFS), Wavelets and Discrete Cosine Transform (DCT). The choice of a particular solution is governed by its suitability in a particular application.In PCA method, also known as Eigenface method, face images are projected onto the so called eigenspace [6] that best encodes the variations among known facial classes, and recognition is achieved by carrying out match of these projected feature vectors. The advantage of this method is real-time recognition, but the method in itself is sensitive to change in illumination, facial orientation and its size. LFA [8] method of recognition is based on analysing the face in terms of local features, e.g., eyes, nose, etc. by what is referred to as LFA kernels. LFA technique offers better robustness against local variations on the facial image in carrying out a match, but does not account for global facial attributes. Face recognition based on template matching [13] represents a face in terms of a template consisting of several masks enclosing the prominent features e.g. the eyes, the nose and the mouth. Matching is usually carried out by a correlation score computed from the pixel intensities of these masks taken from the reference, with the query image. H owever, this method is sensitive to scale, orientation, and nonlinear illumination changes of the face. Recognition by Neural Network [11] are based on learning of the faces in an ‘Example Set’ by the machine in the ‘Training Phase’ and carrying out recognition in the ‘Generalization Phase’. But to succeed in a practical set-up, the examples should be adequately large in number to account for variations in real life situations. Model Matching methods of face recognition (like Hidden Markov Model (HMM) [12]) train a model for every person during model learning and choose the best matching model, given a query image. Success of these methods largely depends on building realistic representative models. Recognition technique formulated on Partitioned Iterated Function System (PIFS) [9] makes use of the fact that human face shows region-wise (fractal) self-similarity, which is utilised for encoding the face to generate the PIFS code. Recognition is performed by matching these PIFS codes. Although PIFS code extracts the signature of the face efficiently, the technique is not robust against the facial variations occurring from instance to instance. Selection of the prominent coefficients from the wavelet transform (WT) is effective in extracting the signature of a face and eliminating the redundancies. Moreover, WT provides a inherent handle to deal the data at different scales. Ref. [12] deals with WT employed to carry out face recognition. DCT-based recognition technique [7] depends on the capabilities of the discrete cosine transform to extract the facial signature in terms of a few DCT coefficients. Recognition is achieved by matching the DCT signatures. Both wavelet and DCT have promising future as the underlying techniques for implementation of successful face recognition systems. Fusing the scores of several different classifiers applied on the same data is a very promising approach to improve the overall accuracy of the biometric systems [18]. We make use of such intramodal fusion [14] combining correlation-based template matching with PCA or DCT methods applied on the same facial data to decide the authenticity of a subject. In this paper, we present a prototype system implementing our technique of face recognition.3.MULTI-ALGORITHMIC FACERECOGNITIONEarlier we developed a technique [1] of verifying human face by matching against templates retrieved from the reference database created during registration process. In this technique the matching is carried out in terms of a set of correlation scores corresponding to different areas of interest (rectangular bit-maps representing different regions of the face) and their Euclidean distances measured in pixels. This technique can be extended for identification by matching the input face against every registered identity to choose the one which gives the best correlation scores and minimum distance error. Unfortunately, correlation is compute-intensive, and to match a face against a large number of reference faces using correlation software takes unacceptably long duration for a practical application.A PCA-based ApproachA survey [2,4] on the available literature revealed the main techniques of face recognition which are mentioned in section II. We decided to implement the PCA method of recognition first. In this method as well as those described in subsections 3B and 3C, our prototype recognition system (described in the later part of subsection 3C) makes use of reasonably constrained imaging set-up with facial image grabbed in frontal geometry under sufficient illumination level in order to minimise the variations in the acquired image. We made use of the open source routine [17] forcarrying out face detection from the captured images as well as for computation of eigenfaces and for projecting a facial image on the face space to obtain a set of weightages. Software modules developed include those for facial size normalization, calculation of threshold parameters for classification and taking recognition decision based on the distance of set of weightages of the projected query face from the distances of set of weightages of the projected reference faces. The complete software was implemented in Visual C++ under MS-Windows. Fig.1. (a) shows a sample registration image gallery and Fig.1. (b) the corresponding eigenfaces from our PCA-based face recognition database.The test results on a set of 109 images (capturedusing our prototype system) belonging to 43 persons, both male and female, indicated that the PCA-based face recognition software was unable to extract the correct match to be the best match in few cases, but the correct identity was there in the top 5 matches for most of these cases. Although the main performance measure for a biometric identification system is the percentage of queries in which the correct answer can be found in the top few matches [3], this top set of matches must be further probed in order to identify the true owner of the rendered query biometric signature. Our aim was to use face recognition for access control. Therefore, our PCA implementation alone was deemed unsuitable under the situation when the final decision on granting access isrequired to be in an automated way.(a) (b)Fig.1. (a) Sample registration image gallery, and (b) the correspondingeigenfaces from database.B DCT-based Approach We also decided to probe the feasibility of DCT-based technique of face recognition for access control application. Open source routine for carrying out face detection from a given image was used. Software modules developed include those for facial size normalization, computation of DCT of a face image and making recognition decision based on the distance of the DCT code of the query face from the reference DCT codes in the database. All the modules were developed and integrated in Visual C++ environment under MS-Windows. Fig.2. (a) depicts a sample face, and (b) the corresponding DCT plot from our DCT-based face recognition database. The test results on the set of 109 images (mentioned in the previous section) indicated that the DCT-based face recognition software was unable to extract the correct face to be the best match in few cases, although the correct identity was there again in the best 5 matches for most of the cases. Therefore, our DCT implementation alone was alsounsuitable for access control application.Fig.2. (a) Sample face, and (b) the corresponding DCT plot from ourdatabase .C Multi-algorithmic approachIt was decided to try a multi-algorithmic approach for face recognition based on PCA followed by template matching using correlation as well as DCT followed by template matching using correlation methods. Here, the basic strategy was to select a few (we chose 5) probable identities as provided by PCA / DCT techniques and to decide the final identity to be that corresponding to the highest score provided by template matching method applied only on these selected 5 identities. This multi-algorithmic scheme of recognition kept the computational load within acceptable limit for adoption as a practical system, and at the same time, improved the recognition accuracy considerably. Ourproposed system is described below in details.Fig.3. Template selection. In the multi-algorithmic approach of multi-biometric face recognition, a user is registered with the system using the different algorithms employed, before they can be identified. For registration, after grabbing the facial image, location of the face is automatically detected and normalised w.r.t. size (128x128 in our system; a standard gray level interpolation algorithm is made use of) and w.r.t. illumination (to a predecided average intensity level). Next, a facial template (comprising of eyes, nose and mouth regions) for the user is selected (see Fig.3) manually. This template is stored in the reference database. After grabbing the facialimages (and normalising w.r.t. size and illumination), PCAanalysis is carried out to compute the eigenvectors, from which the set of weightages (reference PCA feature vector) .for all the registered users are calculated and stored. Also, DCT codes are extracted from the (normalised) facial images and stored in the reference database.During identification, the facial image of the person to be recognised (query) is grabbed. After detecting the face automatically in this image, it is normalised w.r.t. size and illumination. Then, the PCA and DCT signatures are extracted. Matching is carried out with this extracted signatures against the reference database. The top 5 identities are chosen both w.r.t. PCA and DCT matching, separately. The following processing steps are carried out on both these sets of 5 identities.Using the reference templates of the left and right eyes corresponding to all the top 5 identities, locations of the two eyes on the query face are determined using gray level correlation technique. The inter-ocular distance is used to calculate the scale factor of the query w.r.t. the reference image. Using this scale factor, the query is re-sized. Now, matching is carried out on the re-sized query using all the identity templates one by one (for the top 5 identities as found in the previous paragraph) using correlation. A quality degradation factor (QDF) is evaluated by linearly combining the correlation score and the distance error (computed based on the relative position of the features in the reference and query; see Fig.3). The recognised identity is decided to be that corresponding to the best (lowest in our formulation) QDF.All the steps involved in the face recognition process are indicated in the block schematic of Fig.4.We have made use of open source routines [17] for carrying out face detection from the captured image during registration and identification, as well as for computation of the eigenfaces and the set of weightages for an image from these eigenfaces.Fig.5 shows the prototype face recognition system. The system comprises of a Pentium-IV PC executing the recognition software, a table-top stand-mounted video camera, a frame grabber plugged in the PC, sitting arrangement kept at a fixed distance of about one meter from the camera, a flexible enclosure surrounding the seat to minimize the effect of fluctuations of the ambient lighting and a set of four fluorescent light sources providing sufficient illumination within the area enclosed.Fig.4. Block schematic of face recognition algorithm based on combination of correlation and PCA/DCT techniques.Fig.5. PC-based face recognition system4.RESULTSThe technique has shown promising results during laboratory tests conducted for a set of 109 images belonging to 43 different subjects.Fig.6. Recognition rate of various face recognition techniques tried.The recognition rate, measured as the ratio of successful attempts (cases where the best match is the correct match) to the total attempts, was analyzed offline for the various approaches. It was observed that addition of correlation technique improved the recognition rate. As the best 5 matches provided by the individual PCA / DCT technique are the input for correlation technique, the improvement in recognition rate of the combined approach is expected. Moreover, when recognition decision given by PCA followed by correlation technique was combined with that of DCT followed by correlation technique using OR logic, the recognition rate further improved to 89% (Fig.6). This is contributed by those attempt cases in which the correct identity appeared in the best 5 matches provided by only one of the techniques i.e. either the PCA or the DCT but not in the both.With Access Control Application in view, the False Rejection (FR) and False Acceptance (FA) Rates of PCA and DCT were studied as a function of Euclidean distance threshold. Fig.7 shows that for PCA method the FR is 20% and FA is 12% at a distance threshold setting of 7.5.Fig.7. FR and FA rates vs. Distance Thr plot for PCA method.Fig.8. FR and FA rates vs. Distance Thr plot for DCT method.Fig.8 shows the performance achieved by the DCT method. The figure indicates that at distance threshold of 2500, the FR and FA are both approximately 20%. It is also observed from the plot that further increase in the threshold limit does not provide any improvement in the false rejection rate. This behavior is attributed to the attempts with degradation of facial image quality because of inadvertent and inevitable variations in size and orientation, which are bound to occur in a practical scenario.In multi-algorithmic scheme, both the techniques are combined with correlation technique in cascaded fashion i.e. the best 5 results of either the PCA or the DCT technique are provided as the input for the correlation technique, and a threshold is applied to the best QDF calculated for these 5matches.Fig.9. FR, FA rates vs. QDF Thr plot for PCA and Cor method.The FR and FA rates of the combined techniques were studied as the function of the QDF threshold. The plots in Fig.9 and Fig.10 indicate remarkable improvements in FA rate and a marginal improvement in the FR rate. Note that the FA rate is zero for a wide range of QDF values. This makes our multi-algorithmic technique very much suitable for Access Control Application.Fig.10. FR, FA rates vs. QDF Thr plot for DCT and Cor method.5. CONCLUSIONSRecognition of identity of the person based on facial biometric signature can be used in many applications such as access control of secured areas, tele-banking, surveillance, law enforcement, etc. This paper describes a prototype system for carrying out human face recognition based on a combination of correlation with PCA or DCT technique used for the purpose of deciding a facial match. The system has been tested on a data set of 109 images belonging to 43 subjects.Recognition of a person from a 2D projected image of the 3D face is a challenging task because of pose variations of the head along with change in facial appearance. Our prototype recognition system makes use of reasonably constrained imaging set-up with facial image grabbed in frontal geometry under sufficient illumination level in order to minimise the variations in the acquired image. The size of the face in the image acquired during recognition varies due to inadvertent and inevitable head movement occurring between successive instants. Our algorithm tackles this problem by computing the exact scale of the face from inter-ocular distance and normalizing with respect to the registration data. Our technique performs successfully under linear change of intensity level in the face image. However, presently, any nonlinear intensity change cannot be counteracted, although there are few measures suggested regarding it in the literature [10].The authors plan to combine PCA and DCT methods in future along with some of the other suggested techniques for face recognition, e.g. H MM, PIFS, etc. after evaluating the effectiveness of the latter, in order to improve the recognition performance of the face-based multi-algorithmic biometric system.ACKNOWLEDGEMENTThe authors are thankful to Mr. G.P. Srivastava, Director, E&I Group, BARC and Mr. P.S. Dhekne, Associate Director, E&I Group, BARC for their encouragement to take up the task of development of face recognition system. The authors acknowledge the assistance of Mr. Rajesh Babu and Mr. Santosh Gaikwad for setting up the prototype system.REFERENCES[1] S. Kar, Swati Hiremath, D.G. Joshi and V.K.Chadda, “Face Verification Using Template Matching,” 12th International Conference on “Advanced Computing & Communication (ADCOM-2004), Ahmedabad, pp.207-213, Dec 15-18, 2004.[2] D.A. Forsyth and J. Ponce, Computer Vision A Modern Approach, Prentice-Hall India, 2003; Chapter 23, pp. 537-567.[3] P.J. Phillips, A. Martin, C.L. Wilson and M. Przybocki, “An Introductionto Evaluating Biometric Systems,” National Institute of Standards and Technology, USA, IEEE Computer, pp.56-63, Feb 2000.[4] Ming-H suan Yang, D.J. Kriegman and N. Ahuja, “Detecting Faces in Images: A Survey,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. .24, no.1, pp. 34-58, Jan 2002.[5] P. J. Phillips, P. Grother, R. Micheals, Duane M. Blackburn, Elham Tabassi, J. Mike Bone, “Face Recognition Vendor Test 2002: Overview and Summary,” Mar 2003, .[6] M.A. Turk and A.P.Pentland, “Face Recognition Using Eigenfaces,” Vision Modeling Group, The Media Laboratory, Massachusetts Institute of Technology; 1991.[7] Z. M. Hafed and M. D. Levine, “Face Recognition Using the Discrete Cosine Transform,” International Journal of Computer Vision 43(3), 167–188, 2001.[8] P.S. Penev and J.J. Atick, “Local Feature Analysis : A general statistical theory for object representation,” Computational Neuroscience Laboratory, The Rockefeller University, USA.[9] S. Chandran and S. Kar, “Retrieving Faces by the PIFS Fractal Code,” Proceedings of the S ixth IEEE Workshop on Applications of Computer Vision (WACV 2002), Orlando, Florida, pp. 8-12, Dec 2002.[10] S. Malassiotis and M. G. Strintzis, “Robust face recognition using 2Dand 3D data: Pose and illumination compensation,” Pattern Recognition 38 (2005) 2537– 2548.[11] D. Bryliuk and V. Starovoitov, “Access Control by Face Recognition using Neural Networks and Negative Examples,” 2nd International Conference on Artificial Intelligence, Crimea, Ukraine, pp. 428-436, Sept 2002.[12] M. Bicego, U. Castellani & V. Murino; “Using Hidden Markov Models and Wavelets for face recognition,”http://profs.sci.univr.it/~bicego/iciap2003.pdf.[13] R. Brunelli and T. Poggio, “Face Recognition: Features versus Templates,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 15, no.10, pp. 1042-1052, Oct 1993.[14] N. Poh and S. Bengio; “Database, protocols and tools for evaluatingscore-level fusion algorithms in biometric authentication,” Pattern Recognition 39 (2), pp.223– 233,2006.[15] J. Holmes, L. Wright and R. Maxwell, “A Performance Evaluation of Biometric Identification Devices,” Sandia National Laboratories, SAND91-0278 / UC-906, June 1997.[16] J.L. Wayman; “Biometric Data Fusion: Moving Beyond Multimodal Biometrics,” International Workshop on Recent Advances in Biometric Systems, IIT Kanpur, pp.245-250, Apr 2005.[17] Open Computer Vision Library,/projects/opencvlibrary/.[18] A. Lemieux and M. Parizeau; “Flexible multi-classifier architecture for face recognition systems,” Vision Interface, 2003.。

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人脸识别 面部 数字图像处理相关 中英对照 外文文献翻译 毕业设计论文 高质量人工翻译 原文带出处

人脸识别 面部 数字图像处理相关 中英对照 外文文献翻译 毕业设计论文 高质量人工翻译 原文带出处

人脸识别相关文献翻译，纯手工翻译，带原文出处(原文及译文)如下翻译原文来自Thomas David Heseltine BSc. Hons. The University of YorkDepartment of Computer ScienceFor the Qualification of PhD. — September 2005 -《Face Recognition: Two-Dimensional and Three-Dimensional Techniques》4 Two-dimensional Face Recognition4.1 Feature LocalizationBefore discussing the methods of comparing two facial images we now take a brief look at some at the preliminary processes of facial feature alignment. This process typically consists of two stages: face detection and eye localisation. Depending on the application, if the position of the face within the image is known beforehand (fbr a cooperative subject in a door access system fbr example) then the face detection stage can often be skipped, as the region of interest is already known. Therefore, we discuss eye localisation here, with a brief discussion of face detection in the literature review(section 3.1.1).The eye localisation method is used to align the 2D face images of the various test sets used throughout this section. However, to ensure that all results presented are representative of the face recognition accuracy and not a product of the performance of the eye localisation routine, all image alignments are manually checked and any errors corrected, prior to testing and evaluation.We detect the position of the eyes within an image using a simple template based method. A training set of manually pre-aligned images of feces is taken, and each image cropped to an area around both eyes. The average image is calculated and used as a template.Figure 4-1 - The average eyes. Used as a template for eye detection.Both eyes are included in a single template, rather than individually searching for each eye in turn, as the characteristic symmetry of the eyes either side of the nose, provides a useful feature that helps distinguish between the eyes and other false positives that may be picked up in the background. Although this method is highly susceptible to scale(i.e. subject distance from the camera) and also introduces the assumption that eyes in the image appear near horizontal. Some preliminary experimentation also reveals that it is advantageous to include the area of skin justbeneath the eyes. The reason being that in some cases the eyebrows can closely match the template, particularly if there are shadows in the eye-sockets, but the area of skin below the eyes helps to distinguish the eyes from eyebrows (the area just below the eyebrows contain eyes, whereas the area below the eyes contains only plain skin).A window is passed over the test images and the absolute difference taken to that of the average eye image shown above. The area of the image with the lowest difference is taken as the region of interest containing the eyes. Applying the same procedure using a smaller template of the individual left and right eyes then refines each eye position.This basic template-based method of eye localisation, although providing fairly preciselocalisations, often fails to locate the eyes completely. However, we are able to improve performance by including a weighting scheme.Eye localisation is performed on the set of training images, which is then separated into two sets: those in which eye detection was successful; and those in which eye detection failed. Taking the set of successful localisations we compute the average distance from the eye template (Figure 4-2 top). Note that the image is quite dark, indicating that the detected eyes correlate closely to the eye template, as we would expect. However, bright points do occur near the whites of the eye, suggesting that this area is often inconsistent, varying greatly from the average eye template.Figure 4-2 一Distance to the eye template for successful detections (top) indicating variance due to noise and failed detections (bottom) showing credible variance due to miss-detected features.In the lower image (Figure 4-2 bottom), we have taken the set of failed localisations(images of the forehead, nose, cheeks, background etc. falsely detected by the localisation routine) and once again computed the average distance from the eye template. The bright pupils surrounded by darker areas indicate that a failed match is often due to the high correlation of the nose and cheekbone regions overwhelming the poorly correlated pupils. Wanting to emphasise the difference of the pupil regions for these failed matches and minimise the variance of the whites of the eyes for successful matches, we divide the lower image values by the upper image to produce a weights vector as shown in Figure 4-3. When applied to the difference image before summing a total error, this weighting scheme provides a much improved detection rate.Figure 4-3 - Eye template weights used to give higher priority to those pixels that best represent the eyes.4.2 The Direct Correlation ApproachWe begin our investigation into face recognition with perhaps the simplest approach,known as the direct correlation method (also referred to as template matching by Brunelli and Poggio [29 ]) involving the direct comparison of pixel intensity values taken from facial images. We use the term "Direct Conelation, to encompass all techniques in which face images are compared directly, without any form of image space analysis, weighting schemes or feature extraction, regardless of the distance metric used. Therefore, we do not infer that Pearson's correlation is applied as the similarity function (although such an approach would obviously come under our definition of direct correlation). We typically use the Euclidean distance as our metric in these investigations (inversely related to Pearson's correlation and can be considered as a scale and translation sensitive form of image correlation), as this persists with the contrast made between image space and subspace approaches in later sections.Firstly, all facial images must be aligned such that the eye centres are located at two specified pixel coordinates and the image cropped to remove any background information. These images are stored as greyscale bitmaps of 65 by 82 pixels and prior to recognition converted into a vector of 5330 elements (each element containing the corresponding pixel intensity value). Each corresponding vector can be thought of as describing a point within a 5330 dimensional image space. This simple principle can easily be extended to much larger images: a 256 by 256 pixel image occupies a single point in 65,536-dimensional image space and again, similar images occupy close points within that space. Likewise, similar faces are located close together within the image space, while dissimilar faces are spaced far apart. Calculating the Euclidean distance d, between two facial image vectors (often referred to as the query image q, and gallery image g), we get an indication of similarity. A threshold is then applied to make the final verification decision.d . q - g ( threshold accept ) (d threshold ⇒ reject ). Equ. 4-14.2.1 Verification TestsThe primary concern in any face recognition system is its ability to correctly verify a claimed identity or determine a person's most likely identity from a set of potential matches in a database. In order to assess a given system's ability to perform these tasks, a variety of evaluation methodologies have arisen. Some of these analysis methods simulate a specific mode of operation (i.e. secure site access or surveillance), while others provide a more mathematicaldescription of data distribution in some classification space. In addition, the results generated from each analysis method may be presented in a variety of formats. Throughout the experimentations in this thesis, we primarily use the verification test as our method of analysis and comparison, although we also use Fisher's Linear Discriminant to analyse individual subspace components in section 7 and the identification test for the final evaluations described in section 8. The verification test measures a system's ability to correctly accept or reject the proposed identity of an individual. At a functional level, this reduces to two images being presented for comparison, fbr which the system must return either an acceptance (the two images are of the same person) or rejection (the two images are of different people). The test is designed to simulate the application area of secure site access. In this scenario, a subject will present some form of identification at a point of entry, perhaps as a swipe card, proximity chip or PIN number. This number is then used to retrieve a stored image from a database of known subjects (often referred to as the target or gallery image) and compared with a live image captured at the point of entry (the query image). Access is then granted depending on the acceptance/rej ection decision.The results of the test are calculated according to how many times the accept/reject decision is made correctly. In order to execute this test we must first define our test set of face images. Although the number of images in the test set does not affect the results produced (as the error rates are specified as percentages of image comparisons), it is important to ensure that the test set is sufficiently large such that statistical anomalies become insignificant (fbr example, a couple of badly aligned images matching well). Also, the type of images (high variation in lighting, partial occlusions etc.) will significantly alter the results of the test. Therefore, in order to compare multiple face recognition systems, they must be applied to the same test set.However, it should also be noted that if the results are to be representative of system performance in a real world situation, then the test data should be captured under precisely the same circumstances as in the application environment.On the other hand, if the purpose of the experimentation is to evaluate and improve a method of face recognition, which may be applied to a range of application environments, then the test data should present the range of difficulties that are to be overcome. This may mean including a greater percentage of6difficult9 images than would be expected in the perceived operating conditions and hence higher error rates in the results produced. Below we provide the algorithm for executing the verification test. The algorithm is applied to a single test set of face images, using a single function call to the face recognition algorithm: CompareF aces(F ace A, FaceB). This call is used to compare two facial images, returning a distance score indicating how dissimilar the two face images are: the lower the score the more similar the two face images. Ideally, images of the same face should produce low scores, while images of different faces should produce high scores.Every image is compared with every other image, no image is compared with itself and nopair is compared more than once (we assume that the relationship is symmetrical). Once two images have been compared, producing a similarity score, the ground-truth is used to determine if the images are of the same person or different people. In practical tests this information is often encapsulated as part of the image filename (by means of a unique person identifier). Scores are then stored in one of two lists: a list containing scores produced by comparing images of different people and a list containing scores produced by comparing images of the same person. The final acceptance/rejection decision is made by application of a threshold. Any incorrect decision is recorded as either a false acceptance or false rejection. The false rejection rate (FRR) is calculated as the percentage of scores from the same people that were classified as rejections. The false acceptance rate (FAR) is calculated as the percentage of scores from different people that were classified as acceptances.For IndexA = 0 to length(TestSet) For IndexB = IndexA+l to length(TestSet) Score = CompareFaces(TestSet[IndexA], TestSet[IndexB]) If IndexA and IndexB are the same person Append Score to AcceptScoresListElseAppend Score to RejectScoresListFor Threshold = Minimum Score to Maximum Score:FalseAcceptCount, FalseRejectCount = 0For each Score in RejectScoresListIf Score <= ThresholdIncrease FalseAcceptCountFor each Score in AcceptScoresListIf Score > ThresholdIncrease FalseRejectCountF alse AcceptRate = FalseAcceptCount / Length(AcceptScoresList)FalseRej ectRate = FalseRejectCount / length(RejectScoresList)Add plot to error curve at (FalseRejectRate, FalseAcceptRate)These two error rates express the inadequacies of the system when operating at aspecific threshold value. Ideally, both these figures should be zero, but in reality reducing either the FAR or FRR (by altering the threshold value) will inevitably resultin increasing the other. Therefore, in order to describe the full operating range of a particular system, we vary the threshold value through the entire range of scores produced. The application of each threshold value produces an additional FAR, FRR pair, which when plotted on a graph produces the error rate curve shown below.False Acceptance Rate / %Figure 4-5 - Example Error Rate Curve produced by the verification test.The equal error rate (EER) can be seen as the point at which FAR is equal to FRR. This EER value is often used as a single figure representing the general recognition performance of a biometric system and allows for easy visual comparison of multiple methods. However, it is important to note that the EER does not indicate the level of error that would be expected in a real world application. It is unlikely that any real system would use a threshold value such that the percentage of false acceptances were equal to the percentage of false rejections. Secure site access systems would typically set the threshold such that false acceptances were significantly lower than false rejections: unwilling to tolerate intruders at the cost of inconvenient access denials.Surveillance systems on the other hand would require low false rejection rates to successfully identify people in a less controlled environment. Therefore we should bear in mind that a system with a lower EER might not necessarily be the better performer towards the extremes of its operating capability.There is a strong connection between the above graph and the receiver operating characteristic (ROC) curves, also used in such experiments. Both graphs are simply two visualisations of the same results, in that the ROC format uses the True Acceptance Rate(TAR), where TAR = 1.0 - FRR in place of the FRR, effectively flipping the graph vertically. Another visualisation of the verification test results is to display both the FRR and FAR as functions of the threshold value. This presentation format provides a reference to determine the threshold value necessary to achieve a specific FRR and FAR. The EER can be seen as the point where the two curves intersect.Figure 4-6 - Example error rate curve as a function of the score threshold The fluctuation of these error curves due to noise and other errors is dependant on the number of face image comparisons made to generate the data. A small dataset that only allows fbr a small number of comparisons will results in a jagged curve, in which large steps correspond to the influence of a single image on a high proportion of the comparisons made. A typical dataset of 720 images (as used in section 4.2.2) provides 258,840 verification operations, hence a drop of 1% EER represents an additional 2588 correct decisions, whereas the quality of a single image could cause the EER to fluctuate by up to 0.28.422 ResultsAs a simple experiment to test the direct correlation method, we apply the technique described above to a test set of 720 images of 60 different people, taken from the AR Face Database [ 39 ]. Every image is compared with every other image in the test set to produce a likeness score, providing 258,840 verification operations from which to calculate false acceptance rates and false rejection rates. The error curve produced is shown in Figure 4-7.Figure 4-7 - Error rate curve produced by the direct correlation method using no image preprocessing.We see that an EER of 25.1% is produced, meaning that at the EER threshold approximately one quarter of all verification operations carried out resulted in an incorrect classification. Thereare a number of well-known reasons for this poor level of accuracy. Tiny changes in lighting, expression or head orientation cause the location in image space to change dramatically. Images in face space are moved far apart due to these image capture conditions, despite being of the same person's face. The distance between images of different people becomes smaller than the area of face space covered by images of the same person and hence false acceptances and false rejections occur frequently. Other disadvantages include the large amount of storage necessaryfor holding many face images and the intensive processing required for each comparison, making this method unsuitable fbr applications applied to a large database. In section 4.3 we explore the eigenface method, which attempts to address some of these issues.4二维人脸识别4.1功能定位在讨论比较两个人脸图像，我们现在就简要介绍的方法一些在人脸特征的初步调整过程。

人脸识别技术观点英语作文

人脸识别技术观点英语作文

人脸识别技术观点英语作文Here is an English essay on the topic of facial recognition technology, with the content exceeding 1000 words as requested. The essay is written without a title and without any additional punctuation marks in the main body.Facial recognition technology has become increasingly prevalent in our modern society with its wide-ranging applications across various sectors. This advanced biometric identification system utilizes algorithms to map an individual's facial features and compare them against a database of stored facial profiles. While the technology offers numerous benefits in terms of security enhancement and convenience, it also raises significant ethical and privacy concerns that warrant careful consideration.One of the primary advantages of facial recognition technology is its ability to enhance security measures. In the realm of law enforcement, this technology can aid in the identification and apprehension of criminals, potentially leading to improved public safety. By cross-referencing facial data with criminal databases, authorities can quickly and accurately pinpoint suspects, streamlining investigative processes. Additionally, this technology can beimplemented in secure access control systems, ensuring that only authorized individuals can gain entry to sensitive areas or facilities, thereby reducing the risk of unauthorized access.Furthermore, facial recognition technology has proven invaluable in various commercial and social applications. Retailers can leverage this technology to personalize customer experiences, offering targeted advertisements and recommendations based on individual preferences. In the financial sector, banks can utilize facial recognition for secure authentication, reducing the reliance on traditional password-based systems and enhancing the overall security of transactions. Similarly, social media platforms can employ facial recognition to facilitate features such as automatic tagging and photo organization, improving user experience and engagement.However, the widespread adoption of facial recognition technology also raises significant ethical and privacy concerns. One of the primary issues is the potential for infringement on individual privacy. The collection and storage of biometric data, such as facial profiles, without explicit consent or adequate safeguards, can be perceived as a violation of an individual's right to privacy. This concern is amplified by the possibility of unauthorized access or misuse of this sensitive information, which could lead to identity theft, stalking, or other malicious activities.Moreover, the accuracy and reliability of facial recognition systems have come under scrutiny. Studies have shown that these systems can exhibit biases, often performing less accurately for individuals from certain demographic groups, such as women and people of color. This bias can lead to disproportionate targeting or false identifications, which can have severe consequences, particularly in law enforcement contexts. The potential for such errors to perpetuate societal inequalities and undermine the principles of fairness and justice is a significant concern.Another pressing issue is the lack of comprehensive regulatory frameworks governing the use of facial recognition technology. In many countries, the legal landscape surrounding the collection, storage, and application of biometric data remains unclear, leaving individuals vulnerable to potential abuses. Without clear guidelines and oversight, there is a risk of unchecked surveillance, profiling, and discrimination, which can erode the fundamental civil liberties and democratic principles that societies strive to uphold.Furthermore, the potential for the misuse of facial recognition technology by authoritarian regimes or malicious actors is a grave concern. Unchecked access to this technology could enable the monitoring and suppression of dissent, the targeting of minority groups, and the erosion of freedom of expression and association. The dystopian scenarios envisioned in works of science fiction havethe potential to become a reality if appropriate safeguards and ethical considerations are not prioritized.In response to these concerns, there have been growing calls for greater regulation and oversight of facial recognition technology. Policymakers and civil society organizations have advocated for the implementation of robust privacy laws, data protection frameworks, and algorithmic accountability measures. These efforts aim to ensure that the development and deployment of facial recognition systems are aligned with fundamental human rights and that individuals are granted the necessary protections against potential abuses.Additionally, there have been calls for increased transparency and public discourse around the use of facial recognition technology. Engaging with diverse stakeholders, including privacy advocates, technology experts, and affected communities, can help shape policies that strike a balance between the benefits of the technology and the preservation of individual rights and civil liberties.It is also crucial that the developers and deployers of facial recognition systems prioritize the principles of fairness, non-discrimination, and ethical design. This includes addressing the issue of algorithmic bias, improving the accuracy and reliability of the technology, and implementing rigorous testing and auditing procedures to identify and mitigate potential harms.In conclusion, the rapid advancement of facial recognition technology presents both opportunities and challenges. While the technology offers valuable applications in enhancing security and enabling convenient commercial and social experiences, the ethical and privacy concerns it raises cannot be overlooked. Striking the right balance between the benefits of the technology and the protection of individual rights will require a multifaceted approach involving robust regulation, transparent governance, and a commitment to ethical and responsible development. As we navigate the evolving landscape of facial recognition technology, it is crucial that we prioritize the preservation of fundamental human rights and the well-being of individuals and society as a whole.。

Face Recognition(人脸识别)

Face Recognition(人脸识别)

Face RecognitionToday ,I will talk about the study about face recognition.(第二页)As for the face recognition, we main talk about Two-Dimensional Techniques. The study is from The University of York ,Department of Computer Science , as for the date, it is September 2005.(第三页)We say the background.The current identification technology mainly include: fingerprint identification指纹识别, retina recognition视网膜识别, iris recognition虹膜识别, gait recognition步态识别, vein recognition静脉识别, face recognition人脸识别, etc.advantages优点：Compared with other identification methods, face recognition because of its direct, friendly and convenient features, users do not have any psychological barriers, is easy to be accepted by users.(第四页)Two-Dimensional Face Recognition is main about Face Localization.This consists of two stages: face detection（人脸检测）and eye localization（眼睛定位）. (第五页)Today we main study the research of eye localization.Eye localization is performed on the set of training images, which is then separated into two groups. By it, we can compute the average distance from the eye template. one is eye detection was successful (like the picture on), the dark picture means the detected eyes is closed to the eye template; and the other is failed(like the picture down), the bright points down means doesn’t close.(第六页)We do the research using the way: The Direct Correlation Approach（直接相关方法）.This is the way we make the study, you can have a little know about it. So I will not talk much about it.(第七页)This is the study’s main Experimental Process.It is divided into some groups, calculate the distance d, between two facial image vectors, we can get an indication of similarity. Then a threshold is used to make the final verification decision.(第八页)The result wo get the picture. By the picture, we gets an EER (能效比)of 25.1%, this means that one quarter of all verification operations carried out resulted in an incorrect classification. That also means Tiny changes cause the change of the location in image.(第九页)Conclusion: Two-Dimensional Techniques (we say 2D) is an important part in face recognition. It make a large use in face recognition. All in all, Face recognition is the easiest way to be accepted in the identification field.Thank you!。

特征脸算法人脸识别系统

特征脸算法人脸识别系统

第3"卷第2期2018年6月金陵科技学院学报JOURNAL OF JINLING INSTITUTE OF TECHNOLOGYVol. 3"! No.2June，2018D O I: 10.16515#.cnki.32-1722/n.2018.02.0001特征脸算法人脸识别系统胡勇，朱莹莹(金陵科技学院软件工程学院，江苏南京211169)摘要：人脸识别技术，是指对给定的一个人脸图像，从存储的已知身份的人脸图像库中识别出该人身份的一种技术。

其主要应用于证件验证、刑侦破案、门禁系统、视频监控等领域。

实现了基于特征脸算法的人脸识别系统，在实验中采用的人脸图像数据库为E SSE X大学的F A C E S9"人脸图像数据库。

该系统的优点是识别速度快、准确率高，具有一定的实际应用价值。

关键词：人脸识别（特征脸；F A C E S9"人脸图像数据库;算法中图分类号：T P391.1文献标识码：A文章编号"672 - 755X(2018)02 - 0001 - 0"Face Recognition System Based on Eigenface AlgorithmHU Yong，ZHU Ying-ying(Jingling Institute of Technology，N anjing211169, China)Abstract：Face recognition is a technique that identifies a person!s identity from a given face im­age in a face image database.It is widely used in the fields of certificate veri investigation，access control system，video surveillance and so on.A face recognition systembased on the eigenface algorithm is implemented in this paper.The database used in the experi­ment is the FACES94 face image database of ESSEX University.The proposed system has theadvantages of high recognition rate，high accuracy rate and practical application value.Key words：face recognition；eigenface；FACES9" face image database;algorithm树林里没有两片一模一样的树叶，人世间也没有两张一模一样的面孔。