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在演讲中如何使用肢体语言英语作文The Importance of Body Language in SpeechesIn the world of presentations and speeches, words alone often fail to convey the complete message. This is where body language steps in, complementing and sometimes even overtaking the spoken word. Body language, encompassing facial expressions, hand gestures, posture, and eye contact, is a crucial aspect of effective communication.Facial Expressions: The face is the most expressive part of the body. A smile can convey warmth and friendliness, while a serious expression might indicate the gravity of the topic. Frowns and raised eyebrows can emphasize important points or show confusion.Hand Gestures: Hands are powerful tools in speeches. They can be used to emphasize keywords, demonstrate ideas, or simply keep the speaker engaged. However, excessive hand movements can be distracting, so it's essential to strike a balance.Posture: Standing or sitting tall with shoulders back and head held high conveys confidence and professionalism. Slouching, on the other hand, might communicate lack of interest or expertise.Eye Contact: Looking directly at the audience establishes a connection and trust. It's essential to maintain eye contact with different parts of the audience, avoiding long stares at individuals or constantly looking down. Movement: Moderate movement during a speech keeps the audienceengaged. Pacing the stage or using different areas of the platform can add variety and interest. However, constant wandering or shuffling can be distracting.In conclusion, body language is a vital component of any successful speech. It complements the spoken word, enhances messages, and creates a lasting impression on the audience. By mastering the art of body language, speakers can ensure that their messages are not only heard but also understood and remembered.。
Alejandro Jaimes, Nicu Sebe, Multimodal human–computer interaction: A survey, Computer Vision and Image Understanding, 2007.多模态人机交互综述摘要：本文总结了多模态人机交互(MMHCI, Multi-Modal Human-Computer Interaction)的主要方法，从计算机视觉角度给出了领域的全貌。
我们尤其将重点放在身体、手势、视线和情感交互(人脸表情识别和语音中的情感)方面，讨论了用户和任务建模及多模态融合(multimodal fusion)，并指出了多模态人机交互研究的挑战、热点课题和兴起的应用(highlighting challenges, open issues, and emerging applications)。
1. 引言多模态人机交互(MMHCI)位于包括计算机视觉、心理学、人工智能等多个研究领域的交叉点，我们研究MMHCI是要使得计算机技术对人类更具可用性(Usable)，这总是需要至少理解三个方面：与计算机交互的用户、系统(计算机技术及其可用性)和用户与系统间的交互。
考虑这些方面，可以明显看出MMHCI 是一个多学科课题，因为交互系统设计者应该具有一系列相关知识：心理学和认知科学来理解用户的感知、认知及问题求解能力(perceptual, cognitive, and problem solving skills)；社会学来理解更宽广的交互上下文；工效学(ergonomics)来理解用户的物理能力；图形设计来生成有效的界面展现；计算机科学和工程来建立必需的技术；等等。
MMHCI的多学科特性促使我们对此进行总结。
我们不是将重点只放在MMHCI的计算机视觉技术方面，而是给出了这个领域的全貌，从计算机视觉角度I讨论了MMHCI中的主要方法和课题。
陈锻生等:彩色图像人脸高光区域的自动检测与校正方法 2.4 高光区域检测和辐射校正 1905 肤色像素各分量间的协方差阵的特征值实质上就是在其特征矢量上投影值的方差 , 反映了肤色像素在各特征矢量方向上的彩色动态范围.高光分析在我们前面选择的 TL 平面中进行.假设在二维平面上的两个特征值是λ1 和λ2,λ1>λ2,我们发现,比值R=λ1/λ2 是肤色像素在该平面上分布扁平程度的有效度量.对按 L 排序的肤色像素进行 2 维序贯主分量分析之后,可以依次得到 n 对特征值间的 n 个 R 值.如果肤色集合中没有高光区域,则 R 值是单调上升的 .如果肤色集合中的确存在高光区域 ,则 R 值是先升后降 ,其过程会出现一个明显的峰值 .可见 , 检测肤色区域是否存在高光区域就变成检测序贯主分量分析是否存在 R 值的峰值.最大 R 值出现前的一组特征值中较大的特征值所对应的特征矢量就是肤色在 TL 平面中的体反射向量 Vb.如果存在高光区,还需要计算面反射向量 Vs.具体实现是,将按 L 升序排列后的所有肤色像素的(T,S,L三元组进行反序,即从最亮到最暗的像素进行类似上述Vb 的计算,可得到肤色在 TL 平面的面反射向量 Vs. 为了准确定位带高光区域的肤色图像在彩色空间中Γ 形分布的转折点 ,我们在估算体反射向量 Vb 和面反射向量Vs 时避免采用转折点附近的像素,并采用体反射向量 Vb 和面反射向量 Vs 之间的交点作为划分高光和非高光皮肤区域的亮度阈值 Lth. 高光区域的辐射校正的实现是根据双色反射模型将 L 分量大于 Lth 的肤色像素沿着面反射矢量 Vs 的方向投影到体反射矢量 Vb 轴上,同时应注意保留高光区肤色像素间的亮度和色度差异,但这种投影依然会不可避免地降低了高光区的反差.最后我们利用结构阵列中像素对像素的数据组织方式建立了 TSL 到 RGB 彩色查找表, 便于将辐射校正后肤色像素快速从 TSL 映射到 RGB 空间进行显示或存储. 2.5 实验结果利用普渡大学网上的彩色人脸数据库 AR Database[5],一部分用作人脸皮肤训练场地,另一部分用作测试图像.图 5 是部分人脸测试图像、人脸候选区域、自动高光检测和辐射校正效果.从肤色模型检测出的测试图像按 0.1~0.3 之间的某概率阈值二值化,经形状过滤后得到了候选肤色区域(如图 5(b所示.肤色概率阈值大小变化会影响侯选肤色区域的个数和大小 ,但经形状过滤后对后面的高光分析结果影响很小 ,表现出很好的稳健性 . 左图的人脸浓眉和腮帮子胡须等部分对人脸区域虽然有一定影响 , 但缺少部分皮肤面积根本不影响在整体上对皮肤高光区域的检出和辐射校正 . 右图的头发与肤色很接近 , 但经过形状和密集度过滤 , 仅剩下皮肤区域 . 为了与输入图像相比,高光校正后的肤色部分仍然与被过滤的其他部分一起显示(如图 5(c所示. 3 小结本文介绍了一种基于双色反射模型的彩色人脸图像高光检测和辐射校正的全自动方法 . 通过对皮肤光谱反射特性的分析、考察肤色在各种不同彩色空间中的分布形态,我们在 TSL 彩色空间中自动提取含有高光人脸的区域,并提出了一种基于双色反射模型进行高光分析的新方法.实验证明,在关键的 2 维亮度与色度平面中, 即使在基于肤色和形状的人脸区域的分割精度不高的情况下 ,通过对肤色像素进行序贯主分量分析 ,采用特征值比值作主要参数 ,也可以迅速而准确地检测是否有皮肤高光区域的存在 .同时 ,可以鲁棒地确定高光区域与非高光区域的亮度分割阈值,应用双色反射模型进行肤色高光区的辐射校正. 从图 5 中的测试图像可以看出,虽然高光区域被正确地检测和较好地校正,但存在阴影部分,如图中鼻子右侧被漏检和左眼被误检 ,可能影响进一步的人脸局部特征分析 .虽然漏检和误检是难以兼顾的 ,但通过增加训练场地 , 建立不同照明条件的肤色模型 , 系统性能将会得到进一步提高 . 因此 , 利用高光检测和其他照明条件自动检测方法,自适应地选择合适的肤色模型是我们正在继续进行的研究工作. References: [1] [2] Hjelmås E, Low BK. Face detection: A survey. ComputerVision and Image Understanding, 2001,83(3:236~274. Yang MH, Kriegman D, Ahuja N. Detecting faces in images: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002,24(1:34~58.1906 [3] [4] Journal of Software 软件学报 2003,14(11 Klinker GJ, Shafer SA, Kanade T. A physical approach to color image understanding. International Journal of Computer Vision, 1990,4(1:7~38. Strörring M, Ganum E, Andersen HJ. Estimation of the illumination colour using highlights from human skin. In: Proceedings of the 1st International Conference on Color in Graphics and Image Processing. Saint Etienne, 2000. http://www.cvmt.dk/~mst/ Publications/cgip2000html/. [5] [6] [7] [8] Martinez AM, Benavente R. The AR face database. CVC Technical Report #24, 1998./~aleix/aleix_ face_DB.html. Angelopoulou E. Understanding the color of human skin. In: Proceedings of the SPIE Conference on Human Vision and Electronic Imaging VI (SPIE 2001. SPIE Vol. 4299, SPIE Press, 2001. 243~251./~elli/spie.pdf. Tao LM, Peng ZY, Xu GY. The feature of skin color. Journal of Software, 2001,12(7:1032~1040 (in Chinese with English abstract. Terrillon J-C, Shirazi MN, Fukamachi H, Akamatsu S. Comparative performance of different skin chrominance models and chrominance spaces for the automatic detection of human faces in color images. In: Proceedings of the 4th international Conference on automatic face and gesture recognition. IEEE Computer Society, 2000. 54~61./conferen/fg/0580/pdf/ 05800054.pdf. [9] Chen DS, Xie ZP, Liu ZK. Extraction of number plate and character segmentation from color image under complex background. Mini-Micro Systems, 2002,23(9:1144~1148 (in Chinese with English abstract. 附中文参考文献: [7] 陶霖密 ,彭振云 ,徐光祐 .人体的肤色特征 .软件学报 ,2001,12(7:1032~1040. [9] 陈锻生 ,谢志鹏 ,刘政凯 .复杂背景下彩色图像车牌提取与字符分割技术 .小型微型计算机系统 ,2002,23(9:1144~1148.。
PM_Gadgets_03(小工具)数据摘要：various objects photographed outside, mostly on a concrete background.This file provides object labeling and ground truth on the presence of objects in the test images.中文关键词：对象,拍照,具体背景,标签,地面,英文关键词：objects,photographed,concrete background,labeling,ground,数据格式：IMAGE数据用途：图像数据库数据详细介绍：PM_Gadgets_03various objects photographed outside, mostly on a concrete background.This file provides object labeling and ground truth on the presence of objects in the test images.** Training images ************************************258_5810.jpg OliveOil258_5877.jpg CableCutters258_5881.jpg LiptonTeaBag259_5919_3.jpg Tire259_5920_3.jpg Tire259_5921_3.jpg CrownOfRolls259_5922_3.jpg CrownOfRolls259_5923_3.jpg CrownOfRolls259_5924_3.jpg CrownOfRolls259_5925_3.jpg Tomates259_5926_3.jpg Tomates259_5927_3.jpg Hydrate259_5928_3.jpg Hydrate280_8027.jpg Vinegar280_8029.jpg Clock280_8030.jpg Arnica280_8035.jpg LiptonTeaBox280_8038.jpg PineConePierre2Ground006.jpg Concrete PierreObject001_3.jpg CusanoRojo PierreObject002_3.jpg ChopperMachine PierreObject004_3.jpg Bread PierreObject005_3.jpg Bread PierreObject006_3.jpg Bread PierreObject008_3.jpg Blockbuster PierreObject009_3.jpg Bladder PierreObject012_3.jpg Shoe PierreObject015_3.jpg Camelbak PierreObject017_3.jpg Mouse PierreObject020_3.jpg Glove PierreObject021_3.jpg PineCone2 PierreObject023_3.jpg Yogurt PierreObject025_3.jpg Tums PierreObject026_3.jpg BakingPowder PierreObject032_3.jpg Eggs PierreObject034_3.jpg OfficeDuster PierreObject037_3.jpg Nutella PierreObject038_3.jpg PineCone2PierreObject041_3.jpg REItentPierreObject044_3.jpg MopPierreObject046_3.jpg MopPierreObject047_3.jpg CupPierreObject050_3.jpg WaterBottlePierreObject052_3.jpg RemotePierreObject053_3.jpg CoolbreezePierreObject054_3.jpg AlmondOilPierreObject060_3.jpg Clock2PierreObject071_3.jpg BreadPierreObject079_3.jpg Nutella*************************** Test images - single objects ********************************258_5884.jpg LiptonTea(Bag/Box)280_8036.jpg LiptonTeaBox (LiptonTeaBag is acceptable since same logo) 280_8037.jpg LiptonTeaBox (LiptonTeaBag is acceptable since same logo) Pierre2Ground001_3.jpg Concrete2 (not in training set)Pierre2Ground002_3.jpg Concrete3 (not in training set)Pierre2Ground003_3.jpg ConcretePierre2Ground004_3.jpg ConcretePierre2Ground005.jpg ConcretePierreObject003_3.jpg ChopperMachinePierreObject007_3.jpg BlockbusterPierreObject010_3.jpg ShoePierreObject011_3.jpg ShoePierreObject014_3.jpg CamelbakPierreObject016_3.jpg CusanoRojoPierreObject018_3.jpg MousePierreObject019_3.jpg GlovePierreObject022_3.jpg PineCone2PierreObject024_3.jpg ChopperMachinePierreObject027_3.jpg BakingPowderPierreObject028_3.jpg REItentPierreObject029_3.jpg Clock2PierreObject030_3.jpg Clock2PierreObject031_3.jpg EggsPierreObject035_3.jpg Nutella PierreObject036_3.jpg Nutella PierreObject039_3.jpg PineCone2 PierreObject040_3.jpg Tums PierreObject042_3.jpg BakingPowder PierreObject043_3.jpg ChopperMachine PierreObject045_3.jpg Cup PierreObject048_3.jpg Cup PierreObject051_3.jpg WaterBottle PierreObject055_3.jpg Tums PierreObject056_3.jpg AlmondOil PierreObject057_3.jpg CusanoRojo PierreObject058_3.jpg Mouse PierreObject059_3.jpg ChopperMachine PierreObject061_3.jpg Clock2 PierreObject062_3.jpg Yogurt PierreObject063_3.jpg Coolbreeze PierreObject064_3.jpg AlmondOil PierreObject065_3.jpg Shoe PierreObject066_3.jpg Camelbak PierreObject067_3.jpg BlockbusterPierreObject069_3.jpg CusanoRojoPierreObject070_3.jpg CusanoRojoPierreObject072_3.jpg CamelbakPierreObject073_3.jpg BreadPierreObject074_3.jpg EggsPierreObject075_3.jpg EggsPierreObject076_3.jpg BlockbusterPierreObject077_3.jpg TumsPierreObject078_3.jpg PineCone2PierreObject080_3.jpg Nutella********************************* Test images - Scenes ******************************259_5929_3.jpg CrownOfRolls - Hydrate - Tire - Tomates259_5931_3.jpg CrownOfRolls - Hydrate - Tire - Tomates280_8031.jpg Arnica - Clock - Vinegar280_8033.jpg Arnica - Clock - Vinegar280_8034.jpg Arnica - Clock - Vinegar280_8040.jpg BriskTeaBox (BriskTeaBag is acceptable) - PineCone PierreScene001_3.jpg Bladder - Blockbuster - BreadPierreScene002_3.jpg Bladder - BlockbusterPierreScene003_3.jpg Camelbak - CusanoRojo - ShoePierreScene004_3.jpg Glove - MousePierreScene005_3.jpg BakingPowder - Bladder (small part of) - REItent - TumsPierreScene006_3.jpg Camelbak - CusanoRojo - Glove - Shoe PierreScene008_3.jpg Camelbak - CusanoRojo - ShoePierreScene009_3.jpg Glove - MousePierreScene010_3.jpg BakingPowder - Bladder (small part of) - REItent - TumsPierreScene011_3.jpg TumsPierreScene012_3.jpg Eggs - PineCone2PierreScene013_3.jpg Clock - EggsPierreScene014_3.jpg ChopperMachine - Clock - EggsPierreScene015_3.jpg Bread - NutellaPierreScene016_3.jpg BakingPowder - Bladder (small part of) - REItent - TumsPierreScene017_3.jpg Camelbak - CusanoRojo - ShoePierreScene019_3.jpg Cup - MopPierreScene020_3.jpg BakingPowder - Bread - NutellaPierreScene022_3.jpg Glove - WaterBottlePierreScene023_3.jpg Coolbreeze - Glove - RemotePierreScene024_3.jpg Coolbreeze - RemotePierreScene025_3.jpg Coolbreeze - RemotePierreScene026_3.jpg Coolbreeze - RemotePierreScene027_3.jpg BakingPowder - Bladder - Blockbuster - Bread - REItent - TumsPierreScene028_3.jpg BakingPowder - Bread - NutellaPierreScene029_3.jpg Bladder - Blockbuster - Clock2 - Eggs - Pinecone2 PierreScene030_3.jpg AlmondOil - ChopperMachinePierreScene031_3.jpg Glove - WaterBottlePierreScene032_3.jpg Glove - WaterBottlePierreScene033_3.jpg Glove - Remote - WaterBottlePierreScene034_3.jpg AlmondOil - Cup - REItentPierreScene035_3.jpg Coolbreeze - RemotePierreScene036_3.jpg Cup - MopPierreScene037_3.jpg Bladder - BlockbusterPierreScene038_3.jpg Camelbak - CusanoRojoPierreScene039_3.jpg BakingPowder - Nutella - REItentPierreScene040_3.jpg BakingPowder - NutellaPierreScene042_3.jpg Camelbak - CusanoRojoPierreScene043_3.jpg Cup – MopPierre MOREELSPermanent email address: pmoreels (at) vision (dot) caltech (dot) edu mailing address: Caltech MS136-93, 1200 East California Blvd, Pasadena CA91125 - USAI did my PhD under the supervision of Pr. Pietro Perona.I defended my thesis in September 2007. Thanks to everybody in the Vision lab, I enjoyed the years spent there.ResearchMy main PhD research was focused on feature-based object recognition.- List of publications- Dataset- this dataset contains objects photographed on an automated turntable. Views are taken every 5 degree, the objects are photographed by two cameras.- In 2003, I worked with Christian Moeller(art professor at UCLA) on an art exhibition. We called this work "the smiling project". The Pasadena Art Center wrote an article about this exhibition.Publications by Pierre MoreelsPhD ThesisProbabilistic, Features-based Object RecognitionPierre Moreels, California Institute of Technology , 2007Thesis manuscript [PDF, 77.2MB] - Defense presentation [PPT, 11.1MB] Version Universite de Bourgogne, France [PDF, 77.2MB]Conferences and journals papers1. A Probabilistic Cascade of Detectors for Individual Object Recognition Pierre Moreels and Pietro Perona, European Conference on Computer Vision , vol III, pp.426-439, 2008[PDF, 6MB]2. Unsupervised Clustering for Google Searches of Celebrity ImagesAlex Holub, Pierre Moreels (co-first authors) and Pietro Perona, IEEE International Conference on Automatic Face and Gesture Recognition , 2008 [PDF, 1.1MB]3. Probabilistic Coarse-to-Fine Object RecognitionPierre Moreels and Pietro Perona, submitted, International Journal of Computer Vision , 2007[PDF, 6.1MB]4. Evaluation of Features Detectors and Descriptors based on 3D objects Pierre Moreels and Pietro Perona, International Journal of Computer Vision , 2007[PDF, 1.6MB]5. A low-cost test-bed for real-time landmark trackingAmbrus Csaszar, Jay C. Hanan, Pierre Moreels, Christopher Assad, Sensors and Systems for Space Applications, SPIE - 6555 , 2007[PDF, 914KB]6. Evaluation of Features Detectors and Descriptors based on 3D objects Pierre Moreels and Pietro Perona, International Conference on Computer Vision , vol.1, pp.800-807, 2005[PDF, 4.3MB]7. Common-Frame Model for Object RecognitionPierre Moreels and Pietro Perona, Neural Information Processing Systems , 2004[PDF, 4.3MB]8. Recognition by Probabilistic Hypothesis ConstructionPierre Moreels, Michael Maire and Pietro Perona, European Conference on Computer Vision. , 2004[PDF, 532KB] - Powerpoint presentation [PPT, 9.8MB]9. Neural network tracking and extension of positive tracking periodsJay C. Hanan, Tien-Hsin Chao, Pierre Moreels, Optical Pattern Recognition XV, SPIE - 5437 , 2004[PDF, 243KB]10. Watershed identification of polygonal patterns in noisy SAR images Pierre Moreels and Susan E. Smrekar, IEEE. Transactions on Image Processing, Vol.12 Issue 7, pp.740-750, July 2003.[PDF, 122KB]11. Global Characterization of Polygonally Fractured Terrain on Venus and Implications for a Climate Change OriginSuzanne E. Smrekar, Pierre Moreels and Brenda J. Franklin, Proceedings of XXXIVth Lunar and Planetary Science Conference , Houston, TX, March 2003. [PDF, 122KB]12. Characterization and Formation of Polygonal Fractures on Venus Suzanne E. Smrekar, Pierre Moreels and Brenda J. Franklin, Journal of Geophysical Research, 107(E11), 5098, doi:10.1029/2001JE001808, November 2002.[PDF, 2.4KB] - Powerpoint presentation [PPT, 11.1MB]13. WOBBLE - A Proposed Mission to Characterize Past and Present Water on MarsBogdan Udrea, Greg Delory, Geoffrey Landis, Ludovic Duvet, Ahsan Choudhuri, Mauro Prina, Pierre Moreels, Donald Bedard, Gianluca Furano, Proceedings of 53rd International Astronautical Congress, Houston, TX, October 2002.[PDF, 524KB]14. Multi-Scale Segmentation of Venus SAR Images Using a Modified WatershedPierre Moreels and Suzanne E. Smrekar, Proceedings of GRETSI'01 symposium on Signal and Image Processing, Toulouse, France, September 2001.[PDF, 826KB]15. Identification of Polygonal Patterns on Venus Using Mathematical MorphologyPierre Moreels and Suzanne E. Smrekar, Proceedings of XXXIIth Lunar and Planetary Science Conference , Houston, TX, March 2001.[PDF, 1.0MB]Various presentationsCommon-Frame Model for Object RecognitionPierre Moreels and Pietro Perona, NSF Industry day - Caltech October 2003. [JPG, 782KB]News from the web and from magazines- Ciel et Espace (French magazine) [JPG,429KB]- New Scientist magazine [JPG,91KB]- NASA news [PDF,9KB]- Universe (NASA-JPL news) [PDF,344KB]- IEEE CiSE news (Computing Science and Engineering) [PDF,109KB]- SpaceFlight Now (Space news) [webpage]- Planetary Society [PDF,174KB]- Cosmiverse (Space news) [PDF,195KB]- Space Station news [PDF,1.7MB]- Harcourt college news [PDF,98KB]- Russian web news [PDF,188KB]- Other Russian web news [PDF,604KB][PS,3.3MB] - l'ANSA (Italian news) [PDF,27KB]- Astronews (German space news) [PDF,69KB] Miscellaneous- Journal Officiel [JPG,533KB]数据预览：点此下载完整数据集。
博士生申请学位发表学术论文的规定为促进我校博士研究生科研能力与学术水平的提高，保证博士学位论文的质量，博士研究生申请学位论文送审前发表的学术论文须符合以下规定：一、博士研究生以第一作者发表的学术论文须与学位论文相关，且符合各学院的要求(附后)；满足学院要求的学术论文中，须至少有1篇学术论文已公开发表，理工科类博士研究生其中须至少有1篇学术论文用英文发表。
二、在《华南理工大学学报》和其他高校学报，以及华南理工大学主办的其他学术期刊上发表的多篇论文只统计1篇，在学术会议上发表多篇会议论文只统计1篇，且各学科认可的学术会议上发表的学术论文须被SCI/EI收录。
(法学院、马克思主义学院另行规定内容见附表)三、博士研究生以本人为第一作者在本学科国际重要学术期刊上发表1篇学术论文，视为达到申请学位发表学术论文的要求。
四、博士研究生以第一发明人获得授权的与学位论文研究内容相关的发明专利相当于1篇SCI/EI收录的学术论文(计算机科学与工程学院、轻工科学与工程学院、食品科学与工程学院、机械与汽车工程学院另行规定，环境与能源学院博士生不适用于本款内容)。
五、如无特殊说明，认可的期刊目录以录用时的版本为准，且不含增刊、特刊、专刊等；JCR期刊分区及SCI影响因子以录用、发表或提交审核时的最新版本为准；JCR期刊分区指科睿唯安JCR期刊分区。
六、提交审核的学术论文网络在线发表(即具有DOI号、在网络可查阅文章全文)视为公开发表。
七、被录用的学术论文应有编辑部的正式录用函和导师签名的论文投稿原件。
八、论文第一作者/专利第一发明人是指博士研究生本人署名第一，或者导师署名第一、本人署名第二；论文第一署名作者指博士研究生本人署名第一；论文的第一署名单位/专利申请人单位必须是华南理工大学。
九、博士研究生申请答辩时，如果其提交审核的学术论文中尚有正式录用但未公开发表(或发明专利申请公开但未正式授权)的，允许其组织学位论文答辩，答辩通过者，经所在学院学位评定分委员会审议可先准予毕业，但暂不审议其学位，待其在毕业后两年内所提交审核的学术论文全部公开发表(或专利授权)后，再由本人提出申请审议其学位。
1 人脸识别研究的发展状况1.1 发展历史早在1888年和1920年Galton就在《Nature》上发表过两篇关于利用人脸进行身份识别的论文。
真正意义上的自动人脸识别的研究开始于二十世纪六十年代中后期 1965年Chen 和Bledsoe的报告是最早的关于自动人脸识别的文献。
1965到1990年之间是人脸识别研究的初级阶段 这一阶段的研究主要集中在基于几何结构特征的人脸识别方法 Geometric feature based 。
该阶段的研究基本没有得到实际的应用。
1991年到1997年间是人脸识别研究非常活跃的重要时期。
出现了著名的特征脸方法Eigenface 该方法由麻省理工学院的Turk和Pentland提出 之后有许多基于该方法的研究。
Brunelli和Poggio在1992年对基于结构特征的方法和基于模板匹配的方法进行了实验对比 并给出了后者优于前者的明确结论。
该时期内 美国国防部资助的FERET FacE Recognition technology Test 项目资助多项人脸识别研究 创建了著名的FERET人脸图像数据库。
该项目极大地促进了人脸识别算法的改进以及算法的实用化。
1998年至今 研究者开始针对非理想条件下的人脸识别进行研究。
光照、姿势等问题成为研究热点。
出现了基于3D模型的人脸建模与识别方法。
在商业化的应用方面 美国国防部在2000年和2002年组织了针对人脸识别商业系统的评测FRVT Face Recognition Vendor Test) 比较领先的系统提供商有Cognitec, Identix和Eyematic。
1.2 主要公共数据库人脸数据库对于人脸识别算法的研究是不可缺少的 而公共人脸图像数据库的建立方便不同研究者之间的交流学习 并有助于不同算法的比较 下面列举常用的人脸图像数据库。
FERET人脸数据库 是FERET项目创建的人脸数据库 该库包含14,051幅多姿态、不同光照条件的灰度人脸图像 是人脸识别领域应用最广泛的人脸数据库之一。
广西南宁沛鸿民族中学2024-2025学年高二上学期10月月考英语试题一、阅读理解Mobile Birthday PartyBook the Mobile Children’s Museum for your child’s birthday party! San Diego Children’s Discovery Museum can now bring the Museum directly to you.Length of time:Set-up and Clean-up: 30 minutes eachParty: 1.5 hoursHours:Weekends: 10:00 am-3:00 pmPricing:Up to 20 guests: $300Up to 30 guests: $400Up to 40 guests: $500Museum members will enjoy a 10% discount.Birthday Party Package includes:•Arts and Crafts activity•Three Mobile Children’s Museum exhibits。
Ball Wall: Party guests discover the fundamentals of physics through a custom-built Ball Wall. By arranging magnetic (磁的) tracks into patterns, they must use gravity and slope to help a ball travel from one end of the wall to the other.。
Imagination Playground:Party guests use large foam (泡沫) blocks to discover the fundamentals of architecture. Skills are put to the test as kids work together to complete a challenge.。
信ia 与电ns China Computer & Communication 專该語言2020年第22期人脸表观年龄估计综述杜希婷（北京建筑大学，北京100044）摘 要：近年来，人脸表观年龄估计引起了越来越多的关注.基于此，本文对近年来表观年龄估计的相关研究发展 状况进行了综述，包括基于传统算法和基于深度学习算法两方面，然后对常用的数据库和性能评价指标进行了总结，最 后对基于人脸图像的表观年龄估计所面临的挑战和未来的发展方向进行了讨论.关键词：人脸衰老；深度学习；表观年龄估计；年龄数据库中图分类号：TP391 文献标识码：A 文章编号：1003-9767 （2020） 22-052-03An Overview of Face Apparent Age EstimationDU Xiting(Beijing University of Civil Engineering and Architecture, Beijing 100044, China)Abstract: In recent years, the estimation of apparent age of human face has attracted more and more attention. This paper summarizes the research and development of apparent age estimation in recent years, mainly including traditional methods and deep learning methods, and then summarizes the commonly used database and performance evaluation indicators. Finally, the challenges and future development of apparent age estimation based on face image are discussed*Keywords: face aging;deep learning; apparent age estimation;aging database 1估计步骤介绍近年来，表观年龄估计逐渐引起关注，它是一个人看起 来的年龄，而非实际年龄。
大学英语综合教程1-Unit-3习题答案Key to Exercises (Unit 3)Text comprehension:I. AII.F, T, T, T, FIII.1. Refer to Para 1 for the four examples.2. Refer to Para 4. It could not only set anexample for your children and grandchildren but it adds priceless panache to your image.3. Refer to Para 5. The fact that the guesthad included a recipe for a dish the author had complimented her on at an earlier gathering made the author feel all the more appreciative.4. Refer to Para 7. It is the simple phrase"Excuse me".5. Refer to Para 9. It is because to use goodmanners with our own families counts the most, for those are the people we love the most.6. As good manners are infectious, shewishes that everyone would catch them sothat they would spread..IV. 1. a gracious manner adds great splendor to your image.2. I dare say the note my guest sent me didn't take long to write.3. The simple phrase "excuse me" made most of your irritation disappear.4. Being punctual has always beenconsidered a virtue, both in the past andat present; it has not becomeoutdated.VocabularyI. 1. become different from what it should belike2. displaying gratitude by waving a hand ornodding the head; move out onto the main road3. be of great significance4. who receives the thank-you note remark5. produce a far-reaching effect6. practice good mannersII. Punctuality, routine, infectious, bet,terrific, board, valued, count,cherishes, irritationsIII. D, A, B, B, C, A, D, AIV. 1 praised, compliment, praise, complimented2. appreciated, enjoyed, enjoying, appreciated3. priceless, priceless, precious, precious4. see, Notice, see, noticedV. 1. thanks (recognition)2. activeness (liveliness, briskness,eagerness)3. fashionable ( graceful, elegant)4. selfish (mean, ungenerous)5. nice (courteous, polite, friendly)6. leisurely (relaxed, idle, unoccupied,lazy)7. annoyance (displeasure,dissatisfaction)8.promptness ( timekeeping, reliability) VI. unfriendly, boyhood, understanding, reception,disappearance, decision, differing,elevatedGrammarII. 1. older than2.more interested3. as crowded as4. As pale as5. More exciting than6. As complicated7.easier than8.nicer9. more selective10.nicer11.as unlucky as12. more difficultIII.1. stronger2. more noisy3. more expensive4. more difficult5. happier6. younger7. more often8. further9. more exciting10. louderIV.1. more comfortable2. the funniest3. the worst4. more serious5. the most popular6. quieter7. the most beautiful8. healthier9. the least honorable10. less challenging; least challenging V. 1. Older 2. Oldest 3. True 4. True 5. Further 6. A more 7. Better 8.trueVI. e.g. But even worse was the fact that the headmaster had found out the boys' secret plan.Translation exercisesI.1. 譬如，我在纽约就看到这样的差别，与我20多年前刚搬来时大不相同了：人们蜂拥走进电梯，却没有让电梯里的人先出来；别人为他们开门时，从来不说“谢谢”；需要同事给他们递东西时，从来不说“请”；当其他开车人为他们让道时，也从不挥手或点头表示谢意。
Integration of face and hand gesture recognitionYung-Wei Kao1, Hui-Zhen Gu1, and Shyan-Ming Yuan1,21 Department of Computer Science and Engineering National Chiao Tung University, 1001 Ta Hsueh Rd., Hsinchu 300, Taiwan2 Department of Computer ScienceAsia University, Lioufeng Rd., Wufeng , Taichung County, Taiwan {ywkao, hcku}@.tw, smyuan@.twAbstractFace recognition and hand gesture recognition technologies have been developed separately for many years. Usually they are treated as independent systems. In this paper, we integrate the face and hand gesture recognition. We claim that the face recognition rate can be improved by hand gesture recognition. Also, we propose a security elevator scenario. Finally, we simulate this security elevator scenario by PCA method, based on the ORL database, and show that the face recognition rate and overall accuracy is improved after integration. We believe that this is a general method to integrate two recognition engines, not only for face and hand gesture recognition.1. IntroductionFace recognition and hand gesture recognition technologies have been developed a lot separately for many years [1][2][3]. Also, the face recognition has been used to be the authentication mechanism for security surveillance system [4]. Although there are many researches adopted these two recognition systems into some applications, such as robot application [5], in these researches, the face and hand gesture recognition system are regarded as independent functions. In this paper, we discuss the benefit to integrate two recognition systems.For face or hand gesture recognition, there are a lot of technologies can be adopted as the recognition engine, such as PCA (Principle Component Analysis) [6], HMM (Hidden Markov Model) [7], AdaBoost [8], ANN (Artificial Neural Network) [9], etc. Although the recognition technologies are different, most of them follow the basic concept of pattern recognition, that is, to find several linear/nonlinear lines to distinguish the testing/training data into several clusters. The number of clusters depends on the number of patterns defined in the application. Usually, the less clusters cause higher recognition rate. In this paper, we claim that if the number of clusters can be dynamically reduced, the overall recognition rate can be enhanced. We also implement a simulation to show that the result of hand gesture recognition is able to eliminate the number of face cluster, and enhance the recognition rate of face recognition.This paper is organized as follows: chapter 2 describes the system overview of the security elevator scenario. Chapter 3 reviews the PCA method. Chapter 4 shows the system model. Chapter 5 illustrates the evaluation of our simulation program. Finally, the conclusion is drawn.2. System OverviewThe basic pattern recognition technique is to derive several liner/nonlinear lines to separate the feature space into multiple clusters. For example, Figure 1 has nine clusters, from C1 to C9. Assume that these nine clusters belong to different classes.Figure 1. Nine clusters in feature spaceIf point x belongs to C5 in Figure 1 actually, it willbe recognized to be in C4 incorrectly. However, if we978-0-7695-3407-7/08 $25.00 © 2008 IEEE322Third 2008 International Conference on Convergence and Hybrid Information Technology DOI 10.1109/ICCIT.2008.74330dynamically reduce some impossible clusters byFigure 3. System overviewadditional information, the result may be different. For example, in Figure 2, there are five clusters remaining.Figure 2. Five clusters in feature spaceIn this case, although x belongs to no cluster, the distance between x and C5 is the smallest, so it may be correctly clustered into C5. This example provides the intuition of how the recognition rate can be improved. Let’s imagine a simple scenario that there is a security elevator. In this elevator, there is no floor button for pressing. The decision for bringing someone to some floor is taken depends on his face and his hand gesture. The hand gesture indicates the floor he want to reach, and his face is used to decide that is this person permitted to reach the floor indicated or not based on his hand gesture. For such a security system, the recognition rate, especially for face recognition, is very important to perform the security control.Now, we focus on this security elevator scenario mentioned, and develop a simulation system to evaluate the performance of face and hand gesture recognition integration. The system overview is shown in Figure 3.In this system, the input hand gesture and face images are extracted first. After that, these two images are processed by the hand gesture and face recognition engine partially simultaneously. After the hand gesture recognition result is produced, the face recognition engine eliminates the impossible candidates based on the recognized hand gesture dynamically, and figure out which is the recognized person. Finally, we check that is this person permitted to reach the floor indicated or not based on his hand gesture. If he is permitted, then the elevator will bring him to this floor, otherwise, the elevator takes no action.We assume that the security elevator can reach from the 1st floor to the 9th floor. Hence, we defined nine hand gesture symbols, from one to nine, based on the American Sign Language [10]. The hand gesture symbols are listed in table 1. On the other hand, the permitted floor number for each person is based on the face and hand gesture mapping. For example, table 2 is the mapping we generate randomly. Here, we assume that the 1st floor is available for everyone.Table 1. Nine hand gesture patterns1 2 3 4 56 7 8 9Table2. Face and hand gestures map p ing example Face Available Hand GesturesThe integration point takes place at the final stage offace recognition engine. The integration process isshown in figure 4. First of all, we take the recognizedhand gesture as input, and check the available handgesture mapping. If this hand gesture is not availablefor any candidate, then this candidate will beeliminated before the recognition. After that, theremaining processes of traditional face recognition areconducted.Figure 4. Integration processTake Figure 4 for example, without the elimination,the average correctly guess rate is 1/5. On the otherhand, with the elimination method, the averagecorrectly guess rate is 1/2, which is 2.5 times higherthan the previous one. This is the main idea that howthe face recognition rate can be improved.However, there are still risks of this eliminationmethod. For example, in Figure 5, if we incorrectlyeliminate the expected face candidate, the result of theface recognition must be wrong. This kind of error canbe resulted from the wrong recognition result of handgesture recognition engine, or the hand gesture is infact unavailable for this person. Hence, the firstrecognition method chosen to conduct the eliminationis very important. We choose hand gesture recognitionto be the first recognition method, because it is mucheasier to be a high recognition rate method.Figure 5. Integration process with errorThe face recognition accuracy is analyzed in table 4.We discuss the accuracy of face recognition afterintegration if the expected face candidate is eliminatedor not, and the result of original face recognition iscorrect / incorrect. We can see that, when thecandidates are correctly eliminated, and the originalface recognition is correct, the recognition result withelimination method can be correct. Also, if theexpected face pattern is not eliminated, although theoriginal face recognition result is incorrect, the resultof face recognition after integration still has the chanceto be correct.Table 3. Face recognition accuracy analysisCorrect / IncorrecttypeAccuracy of facerecognition afterintegration1.EC + FC Correct2.EC + FI Unknown3.EI + FC Incorrect4.EI + FI IncorrectEC/ EI: Eliminated candidates don’t / doinclude expected pattern.FC / FI: The result of original facerecognition is correct / incorrect3. PCA (Principal Component Analysis)This section reviews the PCA method [6], whichhas been widely used in applications such as facerecognition and image compression. PCA is a commontechnique for finding patterns in data, and expressingthe data as eigenvector to highlight the similarities and differences between different data. The following steps summarize the PCA process.1. Let {D 1, D 2,…D M } be the training data set. Theaverage Avg is defined by:∑==Mi Di MAvg 11(1)2. Each element in the training data set differs fromAvg by the vector Y i =D i -Avg . The covariance matrix Cov is obtained as:TMi YiYi MCov ∑=⋅=11(2)Since the covariance matrix Cov is square, we cancalculate the eigenvectors and eigenvalues for this matrix.3. Choose M’ significant eigenvectors of Cov asE K ’s , and compute the weight vectors W ik for each element in the training data set, where k varies from 1 to M’.W ik = E k T‧(D i - Avg), ∀i ,k(3)Based on PCA, many face recognition techniqueshave been developed, such as eigenfaces [1]. The following steps summarize the eigenface recognition process:1. Initialization: Acquire the training set of faceimages I 1,I 2,…I M . Calculate each face difference vector from the average face Avg by (1), and the covariance matrix Cov is obtained by (2). Then compute the eigenvectors E k of Cov , which define the face space. Finally, compute the weights W ik by (3) for each image in the training set.2. Input querying: When a new testing face image isencountered, calculate a set of weights W testK depending on the same steps mentioned above. The weights W testk forming a vector T p =[w 1,w 2,…,w M’]T describes the contribution of each eigenface in representing the input face image3. Recognition: A simplest technique to classify theweight pattern is to compute the minimum distance of W testK. from T P. It means that the test image can be classified to be in class p when min( Dp ) < Θi , where Dp=|| W testK - T P || and Θi is the threshold.Figure. 6 shows a simplified version of face space to illustrate the projecting results of three training face images W 1, W 2, W 3 and a testing image W testk . We can recognize W testk . as one of the three known individuals W 1,W 2 and W 3 by the projecting distance between W testk with each training images. In this case, there are two eigenfaces e 1,e 2 to construct the face space. The distance between W testk and W 2 is larger than the threshold Θi , they are not considered to be the same person consequently. Furthermore, the projecting location of W testk in the face space is more close to the projecting location of W 1 than W 2. Therefore, we believe that W testk . and W 1 are the same person.Figure 6. A simplified version of face space4. System ModelIn this section, we analyze our system from the probability point of view. Because the hand gesture recognition rate will not be influenced after integration, we only focus on the face recognition rate improvement and the overall accuracy improvement. Assume that the number of face patterns is Nf , theaverage correctly guess rate is 1)(−Nf . However, if the expected value of the number to eliminate face candidates by recognized hand gesture is δE , then the correctly guess rate can be improved to 1)(−−δE Nf . This provides the intuition of the concept that the face recognition rate can be improved by eliminating the face candidates.Let the recognition rate of original face recognition engine be Pf , and the face recognition rate after integration be 'Pf . Moreover, let the recognition rate of hand gesture recognition engine be Ph .In this paper, the overall accuracy is calculated only when both the face and hand gesture recognition results are correct. That is:Ph Pf Po ×= ; Ph Pf Po ×=''So that, if 'Pf is enhanced, the 'Po can also be improved. Also, because 1'≤Pf , so that the overall recognition rate 'Po is impossible to exceed the handgesture recognition rate, Ph Po ≤'. Moreover, if we want Pf Po >', then:Pf Ph Pf >×', so '/Pf Pf Ph >Because 1'≤Pf , so that Pf Ph >. We know that ''Po Pf ≥, so if we want Pf Pf >', the hand gesture recognition rate must be higher than the original face recognition rate, which makes sense because face patterns are usually more complicated than hand gesture patterns.If this integration concept is extended into other applications, it should be noticed that the recognition rate of the first recognition must be higher than the recognition rate of the second recognition method. Otherwise, after integration, the overall accuracy or the recognition rate of the second recognition method will not gain any improvement.5. Evaluation of simulationWe simulate the secure elevator scenario with C++ program. Both the face and hand gesture recognition are performed by the basic PCA method. The face or hand detection is not the topic we focus on, so we use different database for face and hand gesture. In the experiment, the face database we used is ORL database, [11] which contains forty face patterns andten images per pattern.Table 4 and figure 7 illustrate the recognition rate oforiginal face recognition, integrated face recognition, and hand gesture recognition. We analyze the recognition rate if there are one to five training images of these three recognition method. In general, Ph is always higher than Pf and Pf’, and Pf’ almost outperforms than Pf . However, we may notice that if Pf is high enough, the Pf’ is even a little bit lower than Pf .Table 4. Recognition rate ofPf , Pf’, and Ph# of training data1 2 3 4 5 Pf 0.703 0.82 0.873 0.905 0.963 Pf ‘ 0.757 0.83 0.885 0.915 0.96 Ph 0.907 0.933 0.958 0.953 0.983Figure 7. Recognition rate of Pf , Pf’, and PhTable 5 and figure 8 illustrate the recognition rate ofthe original and integrated overall accuracy. We can see that, the same , Pois almost higher than Po’. Also, we may notice that Po’ is able to be higher than 94% under five training data.Table 5. Recognition rate of Po , and Po’# of training data1 2 3 4 5Po 0.638 0.765 0.836 0.862 0.947 Po' 0.687 0.774 0.848 0.872 0.944Figure 8. Recognition rate of Po , and Po 6. ConclusionIn this paper, we integrate two recognition systems: face and hand gesture recognition. We claim that the face recognition rate can be improved after the integration. During the integration, the result of hand gesture recognition and the available hand gesture mapping is used to eliminate face candidates dynamically. Also, we introduce a security elevator scenario, and simulate this scenario by PCA method.The result of simulation shows that both face recognition rate and overall accuracy can be improved.Although the simple PCA method is used in the simulation, we believe that other recognition engines, which are based on the similar candidate concept, are also able to be benefited by our integration method.AcknowledgmentsThis work was mainly supported by National Science Council grant NSC96-2221-E-009-142: pub/sub-based P2P platform and its applications, and partially supported under grant NSC96-2520-S-009-007-MY3.7. ReferencesList and number all bibliographical references in 9-point Times, single-spaced, at the end of your paper. When referenced in the text, enclose the citation number in square brackets, for example [1]. Where appropriate, include the name(s) of editors of referenced books.[1] Turk, M. A. and A. P. Pentland, “Face recognition using eigenfaces”, in Proc. IEEE Conf. CVPR, June 3-6, 1991, pp.586-591.[2] Brunelli, R. and T. Poggio, “Face recognition: features versus templates,” IEEE Trans. Pattern Analysis and Machine Intelligence, Vol.15, No.10, pp.1042-1052, 1993. [3] W. T. Freeman and M. Roth, “Orientation histograms for hand gesture recognition”, Tech. Rep. TR-94-03a, Mitsubishi Electric Research Laboratories, Cambridge Research centre, Cambridge, Mass, USA, 1995.[4] A.M. Burton, S. Wilson, M. Cowan, V. Bruce, “Face recognition in poor-quality video: Evidence From Security Surveillance”, Psychological Science, Vol. 10, No. 3, May 1999, pp. 243-248.[5] Triesch J. and Malsburg C. V. D, “A Gesture Interface for Human-Robot Interaction”, Proc. of 3th IEEE Intl. Conf. on Automatic Face and Gesture Recognition, pp. 546-551. 1998.[6] Fukunaga, Keinosuke, “Introduction to Statistical Pattern Recognition,” Elsevier,1990.[7] L. R. Rabiner and B. H. Juang, “An introduction to hidden Markov models”, IEEE ASSP Mag., pp 4--16, Jun. 1986.[8] Yoav Freund and Robert E. Schapire, “A short introduction to boosting”, Journal of Japanese Society for Artificial Intelligence, 14(5):771--780, September, 1999. [9] Gurney K., An Introduction to Neural Networks, UCL Press, 1997, ISBN 1 85728 503 4.[10] American Sign Language, /[11] ORL database, /research/dtg/ attarchive/facedatabase.html。
