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International Conference on Computer Systems and Technologies- CompSysTech’07 Combined Fac

International Conference on Computer Systems and Technologies- CompSysTech’07 Combined Fac
International Conference on Computer Systems and Technologies- CompSysTech’07 Combined Fac

Combined Face Recognition Using Wavelet Packets and Radial

Basis Function Neural Network

Ognian Boumbarov, Strahil Sokolov, Georgy Gluhchev

Abstract: In this paper we present a new approach to recognition of frontal faces in color images. It involves face extraction, creation of face models with wavelet packet decomposition for dimensionality reduction, creation of neural classifiers with radial basis functions and combination of classifier results. The first step of the method is face detection through skin-color modeling and segmentation. After the face is extracted, wavelet decomposition is performed. Then, neural classifiers are created respectively for the approximation and details of the wavelet packet decomposition. In the end, combination of classifier results is used to raise overall system availability.

Keywords: Face Detection, Single Gaussian Model, Wavelet Packets, Radial Basis Function Neural Network, Classifier Combination

1. INTRODUCTION

Human face plays a major role in many image and video applications. There are three major application areas for the specific characteristics of facial information [1, 2]: face recognition, content-based description and content-based coding. Face detection usually is a first-step in many applications requiring location and extraction of the face region from the background. It also has several applications in areas such as image retrieval, index and search images in multimedia databases, videoconferencing, crowd surveillance, and intelligent human–computer interfaces. The human face is a dynamic object and has a high degree of variability in its image appearance such as pose variation (front and profile), overlapping, image orientation, lighting condition and facial expression.

Various techniques have been proposed for face classification. They can be divided into three groups: feature based, appearance based and template based [2, 5, 6].

Wavelet transformation of the extracted face image is an art of its representation. This method of transformation became popular during the last 10 years. The purpose of this transformation is to allow the generation of such facial characteristics that are invariant to lighting conditions and overlapping. The wavelet transformation organizes the image in subgroups that are localized according to orientation and frequency. In every subgroup, each coefficient is also localized in space [7, 8, 9].

In this paper we describe an approach for face recognition that works with tree steps: face detection, modeling step with wavelets and combined face classification.

The first step used statistical color models and geometrical information about the face shape [3, 4]. Color information is an important feature of the human face. Skin color usage for detecting face skin regions has several advantages. In particular, processing color is faster than processing other facial features. Also, color information is invariant to face geometrical transformations. This part of the method is described in section 2.1 of the article. In section 2.2., wavelet packet decomposition of the extracted face image is explained. In the following section 2.3., we create two neural classifiers with radial basis functions – one for the approximations – and one for the average sub-images with details of the wavelet decomposition. In the end, in section 2.4 we describe the method of combination of the classifier results.

2. DESCRIPTION OF THE PRESENTED APPROACH FOR FACE RECOGNITION

Our approach consists of several phases. The algorithm uses a database of face images for training and recognition. The test images are acquired by the system via TV

camera. The new image is first subjected to face detection and extraction. This sub-phase consists of filtering, lighting compensation, creation of single Gaussian probability skin model with skin samples, gathered from the image database. Then, every pixel from the input image is compared to the single Gaussian skin model. Human skin color model requires a color classification algorithm to be applied. With this algorithm the color model can transform the color image into a gray scale image (skin probability image), after that a skin probability mask could be built using a threshold technique, where all holes in the images are filled with morphological operations. Then the face detection process finishes with a validation of the geometrical information: aspect ratio and elliptical shape. At the end of this phase the face image is extracted from the original image with the binary mask.

The next phase processes only the luminosity (Y-) component of the extracted face image. Normalization, "lighting" compensation, filtering and size normalization are performed, too.

After that face representation is applied through wavelet packet decomposition. Second-level wavelet decomposition is used. One approximation and 15 coefficients are generated.

During next stage the approximations and the average details sub-images from the wavelet packet decomposition are used for creation, training and recognition of neural network classifiers with radial basis functions. There are two types of classifiers created – specific classifiers for every single class and a common classifier for all training classes.

At the end, a decision fusion algorithm is used to find the final classification result.

Fig.2.1. Wavelet packet Decomposition, face classification and decision

2.1. FACE EXTRACTION

The first step of our method is the detection of the face in the input image. In order to create a successful model of the human skin, we need to make proper selection of the color space, which shall be used. The selected color space should have the least possible dimensionality and color component independence from the intensity. The r b C YC color space fulfills these requirements. A typical solution would be to build an approximation of the distribution of the color components b C and r C through a bidimensional normalized Gaussian for modeling of the color of the human skin [1, 2, 6].

Let )(C P skin be the probability for the vector-representation of a pixel with color

components [t R B C C C ,=] belonging to the class of the human skin color. The distribution

of the components of this vector in the R B C C ,- space can be described in the following way:

(1) are the mean value and the covariance matrix subspace. Vector []t R B C C C ,= is

belong to the area. The creation of a binary mask is described in [3, 4]. The shape of human face may be approximated by an ellipse . The extents of the major and the minor axes of the ellipse can be approximated by the extents of the same skin face-candidate region along the axis directions. The degree of the ellipse’s fit is determined by the number of pixels that fall into that shape specified by the computer parameters. This shape feature allows performing validation of all candidate face regions in two steps: coarse validation with the aspect ratio and fine validation with the elliptic shape of the contours.

The aspect ratio should also have a limit. We have determined by analyzing the results in our experiments that a good limit should be between 1.2 and 1.4. There are some situations however, that we indeed have a human face, but the ratio is higher. This happens when the person has no shirt or is dressed in such a way that part of the neck or the body under it is uncovered. In order to account for these cases, we set the ratio to be

1.6 and eliminate the regions of aspect ratio smaller than this value.

The regions of pixels, which remain after this verification, are checked for elliptic shape again. During this operation we are not interested in the color component in the image, because the geometrical analysis is executed on intensity component (Y) only. A binary mask is applied to the (Y) components of the identified skin-like areas from the original image. The face image is extracted from the original image with the binary mask

2.2. LEVEL 2 WAVELET PACKET DECOMPOSITION

In our approach we use wavelet packet decomposition on the extracted face image. Wavelet Packet Decomposition of an image is a useful technique for dimensionality reduction. It is also used to de-noise the analyzed image of the face and to preserve and point out its most important characteristics: face contours, eyes, and mouth and nose regions. The wavelet coefficients are further applicable for recognition and face tracking since they provide invariance to face deformation, lighting conditions, etc [7, 8].

During wavelet packet decomposition the original image is decomposed into 4 sets of wavelet coefficients that represent approximation and details. At each step the previous 4 sub-images of wavelet coefficients are further decomposed into 4 new sub-images: approximation and details:

1|2,1|1,211|2,1|1,2

21|2,1|1,231|2,1|1,2

********n x y n n x y n n x y n n x y n A H H A D H G A D G H A D G G A ????????=???

?????=???????=????????=????? (2)

where:

? A n is approximation at level n, and А0=I(x,y) (original image).

? D ni : Level n details. The parameter i stands for the direction of the

details (i =1, 2, 3 means vertical, horizontal and diagonal respectively).

? |2,1 and |1,2: image transformation was performed both In vertical and

in horizontal direction.

During this separation of the wavelets in every next level two new wavelets are formed:

12()(),()()n n n n

t h t n t g t n ψψψψ=?=?∑∑ During our research we performed face recognition analysis through decomposition with various wavelet series: Daubechies, Gabor, Coiflets, Symlets and Gauss. Our approach provides relatively robust results with all of the tested wavelet series. At that, best results were delivered by the series of Daubechies and the Gaussian (Mexican hat) wavelets.

For the purpose of face recognition we use the second-level approximations of the faces, and the average faces of the wavelet coefficients sub-images. We preferred this recognition approach to the usage of particular wavelet decomposition details (horizontal, vertical or diagonal) since it provided storage of more characteristic features and higher precision of recognition results.

2.3. NEURAL CLASSIFIERS WITH RADIAL BASIS FUNCTIONS

We use combined classification with two RBFNN classifiers: for the approximations at level 2 and for the average images of details at level 2. The two classifiers work with identical algorithms. In the combined approach (in order to perform decision fusion), the individual scores are combined to generate a single scalar score which is then used to compute the final decision. The scores are generated by different images, therefore image and score normalization is required. A single-sample multi-model ( WP decomposition into approximation and details) approach is used on an intra-modal biometric system (face only). On the other hand identical classification algorithms (RBFNN) are used, which require input data normalization (geometrical image sizes and light conditions). The input data normalization is realized with histogram equalization and image size normalization. The image size is estimated based on the current iris distance for each face image.

The combined classification can increase accuracy using two parallel modules (distributed system). A distributed system is where two or more modules work in a parallel manner so that the error is distributed. The reliability of a distributed system s R is [9]:

2

11(1)s i i R R ==??∏, (10)

where i R is the score from each classifier, i =1,2, and ,[0...1]s i R R ∈. The result is correct if the following condition is fulfilled:

21max s i i R R =≥.The R1 classifier is trained with and classifies the second-level approximations of the whole face images (low resolution), where the R2 classifier works with the average images of details of the wavelet packet decomposition. The classifiers R 1 and R 2 are realized with RBFNNs. They contain analogical structures and learning algorithms.

3. EXPERIMENTAL RESULTS

In our approach we have used the GTAV Face Database found at ( http://gps-tsc.upc.es/GTAV/ResearchAreas/UPCFaceDatabase/GTAVFaceDatabase.htm ) [10].

We have used 10 faces with 4 training images and 1 test image for each subject. The first stage of the algorithm – face extraction is shown on fig. 3.1. on subject 1 from the experiment.Fig. 3.1a shows the original image, fig.3.1b shows the probability skin mask computed through the Gaussian distribution of the skin probability.

The level 2 wavelet packet decomposition (for level 1 and 2) of the extracted face image is shown on fig. 3.2.

After the wavelet decomposition we create two separate classifiers for the approximations (high frequency coefficients) of the level 2 WP Decomposition and for the average images of details of the level 2 WP Decomposition. For the first neural classifier we use the approximations as training set, for the second type of classifier we create the so called Wavelet Coefficients Average Face for training. We apply a feed-forward back-propagation neural network with radial basis functions. Two further classifier types were tested during our research: specific and common. We have decided to use a specific classifier for every single class of images since this approach offered more simplicity and fast learning of the neural network. Our network has number of inputs equal to the size of the array of wavelet packet decomposition coefficients. The number of hidden neurons is 100, the training goal was 0.0001. Training is accomplished in 2400 epochs. Table 3.1 contains the results from the classifier for the approximations, table 3.2. holds the results from the classifiers for average wavelet coefficients for each class, as well as a combined classification.

Fig.3.1 Face extraction. Fig 3.2 WP decomposition level 1(a) and level 2 (b)

3.1a (row 1) – original images,

3.1b (row 2) – probability skin maps,

3.1c (row 3) – binary mask after Otsu thresholding,

3.1d (row 4) – binary masks after morphological operations,

3.1e (row 5) – extracted face images

Fig.3.3. Training Set of images, GTAV face database

Table 3.1. Results of the specific neural

Tab. 3.2. Neural classifiers for the average coefficients of the classifiers for the approximation wavelet decomposition and combined classification

Subject Class1 App Class2

App Class3 App

0.9998 0.0014 0.0023

0.9989 0.0211 0.0181

0.0096 0.9853 0.0265

0.0013 0.9539 0.0030

0.0084 0.0038 0.9861 0.0010 0.0023 0.9964

Test

subject Class1 WAVF Class2 WAVF Class3 WAVF Combined Classification

0.9982 0.0183 0.0217 0.9999 0.0335 0.0479 0.9997 0.0014 0.0031 0.9999 0.0236 0.0239 0.0323 0.9841 0.0100 0.0620 0.9999 0.0459 0.0261 0.9912 0.0137 0.0391 0.9999 0.0291 0.0114 0.0127 0.9896 0.0039 0.0279 0.9999 0.0181 0.0256 0.9982 0.0358 0.0430 0.9999

4. CONCLUSION AND FUTURE WORK

In this paper we presented a novel approach to face recognition in color images with wavelet packets and radial basis neural network. It involves face detection with Gaussian probability distribution of the human skin color and geometrical information for face shape. Then wavelet packet decomposition is performed on the extracted face image, after which two classifier types are created. They are equal in structure but serve different purposes – the first classifier is for the approximation of the wavelet packet decomposition, the other one – for the average images of the details of the wavelet decomposition. Through our approach we achieved a very high recognition rate – over 98% for the training set. It is particularly suitable for applications in security, surveillance, videoconferencing, etc.

Until now our research has been focused on the recognition of still face images. Our future efforts will be transferred to active detection, tracking and recognition of face images in dynamic video sequences and real-time tasks.

5. ACKNOWLEDGEMENTS

This work was supported by National Ministry of Science and Education of Bulgaria under contract BY-TH-202/2006: "Methods and algorithhms for analysis of combined biometric information".

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ABOUT THE AUTHORS

Assoc. Prof. Ognian Boumbarov, PhD, Faculty of Communications and Communications Technologies, Technical University of Sofia, Kl. Ohridski 8, 1000 Sofia, Bulgaria, olb@tu-sofia.bg,

Assoc. Prof. Georgi Gluhchev, PhD, Institute of Information Technologies, Bulgarian Academy of Sciences, Acad. G. Bonchev Str., Bl.2, Sofia 1113, gluhchev@iinf.bas.bg Eng. MSc. Strahil Sokolov, graduate of the Faculty of German Engineering Education and Industrial Management, Technical University of Sofia, Kl. Ohridski 8, 1000 Sofia, Bulgaria, strahil.sokolov@https://www.doczj.com/doc/da15781890.html,

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