Proceedings of Fifth IEEE International Conference on Automatic Face Gesture Recognition最新文献

Stride and cadence as a biometric in automatic person identification and verification 步幅和节奏作为生物特征在人的自动识别和验证

Proceedings of Fifth IEEE International Conference on Automatic Face Gesture Recognition Pub Date : 2002-05-21 DOI: 10.1109/AFGR.2002.1004182

Chiraz BenAbdelkader, L. Davis, Ross Cutler

{"title":"Stride and cadence as a biometric in automatic person identification and verification","authors":"Chiraz BenAbdelkader, L. Davis, Ross Cutler","doi":"10.1109/AFGR.2002.1004182","DOIUrl":"https://doi.org/10.1109/AFGR.2002.1004182","url":null,"abstract":"Presents a correspondence-free method to automatically estimate the spatio-temporal parameters of gait (stride length and cadence) of a walking person from video. Stride and cadence are functions of body height, weight and gender, and we use these biometrics for identification and verification of people. The cadence is estimated using the periodicity of a walking person. Using a calibrated camera system, the stride length is estimated by first tracking the person and estimating their distance travelled over a period of time. By counting the number of steps (again using periodicity) and assuming constant-velocity walking, we are able to estimate the stride to within 1 cm for a typical outdoor surveillance configuration (under certain assumptions). With a database of 17 people and eight samples of each, we show that a person is verified with an equal error rate (EER) of 11%, and correctly identified with a probability of 40%. This method works with low-resolution images of people and is robust to changes in lighting, clothing and tracking errors. It is view-invariant, though performance is optimal in a near-fronto-parallel configuration.","PeriodicalId":364299,"journal":{"name":"Proceedings of Fifth IEEE International Conference on Automatic Face Gesture Recognition","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124030136","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 275

Motion-based recognition of people in EigenGait space EigenGait空间中基于动作的人识别

Proceedings of Fifth IEEE International Conference on Automatic Face Gesture Recognition Pub Date : 2002-05-21 DOI: 10.1109/AFGR.2002.1004165

Chiraz BenAbdelkader, L. Davis, Ross Cutler

{"title":"Motion-based recognition of people in EigenGait space","authors":"Chiraz BenAbdelkader, L. Davis, Ross Cutler","doi":"10.1109/AFGR.2002.1004165","DOIUrl":"https://doi.org/10.1109/AFGR.2002.1004165","url":null,"abstract":"A motion-based, correspondence-free technique or human gait recognition in monocular video is presented. We contend that the planar dynamics of a walking person are encoded in a 2D plot consisting of the pairwise image similarities of the sequence of images of the person, and that gait recognition can be achieved via standard pattern classification of these plots. We use background modelling to track the person for a number of frames and extract a sequence of segmented images of the person. The self-similarity plot is computed via correlation of each pair of images in this sequence. For recognition, the method applies principal component analysis to reduce the dimensionality of the plots, then uses the k-nearest neighbor rule in this reduced space to classify an unknown person. This method is robust to tracking and segmentation errors, and to variation in clothing and background. It is also invariant to small changes in camera viewpoint and walking speed. The method is tested on outdoor sequences of 44 people with 4 sequences of each taken on two different days, and achieves a classification rate of 77%. It is also tested on indoor sequences of 7 people walking on a treadmill, taken from 8 different viewpoints and on 7 different days. A classification rate of 78% is obtained for near-fronto-parallel views, and 65% on average over all view.","PeriodicalId":364299,"journal":{"name":"Proceedings of Fifth IEEE International Conference on Automatic Face Gesture Recognition","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123968290","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 210

Detection of people carrying objects : a motion-based recognition approach 携带物体的人的检测:一种基于动作的识别方法

Proceedings of Fifth IEEE International Conference on Automatic Face Gesture Recognition Pub Date : 2002-05-21 DOI: 10.1109/AFGR.2002.1004183

Chiraz BenAbdelkader, L. Davis

引用次数: 54

Interacting with steerable projected displays 与可操纵的投影显示器交互

Proceedings of Fifth IEEE International Conference on Automatic Face Gesture Recognition Pub Date : 2002-05-20 DOI: 10.1109/AFGR.2002.1004187

R. Kjeldsen, Claudio S. Pinhanez, G. Pingali, J. Hartman, A. Levas, Mark Podlaseck

引用次数: 75

Hierarchical wavelet networks for facial feature localization 基于层次小波网络的人脸特征定位

Proceedings of Fifth IEEE International Conference on Automatic Face Gesture Recognition Pub Date : 2002-05-20 DOI: 10.1109/AFGR.2002.1004143

R. Feris, J. Gemmell, K. Toyama, V. Krüger

引用次数: 131

Head tracking by active particle filtering 基于有源粒子滤波的头部跟踪

Proceedings of Fifth IEEE International Conference on Automatic Face Gesture Recognition Pub Date : 2002-05-20 DOI: 10.1109/AFGR.2002.1004137

Zhihong Zeng, Songde Ma

引用次数: 24

On probabilistic combination of face and gait cues for identification 人脸与步态线索的概率组合识别

Proceedings of Fifth IEEE International Conference on Automatic Face Gesture Recognition Pub Date : 2002-05-20 DOI: 10.1109/AFGR.2002.1004151

Gregory Shakhnarovich, Trevor Darrell

引用次数: 102

A modification of kernel-based Fisher discriminant analysis for face detection 基于核的Fisher判别分析在人脸检测中的改进

Proceedings of Fifth IEEE International Conference on Automatic Face Gesture Recognition Pub Date : 2002-05-20 DOI: 10.1109/AFGR.2002.1004170

Takio Kurita, Toshiharu Taguchi

{"title":"A modification of kernel-based Fisher discriminant analysis for face detection","authors":"Takio Kurita, Toshiharu Taguchi","doi":"10.1109/AFGR.2002.1004170","DOIUrl":"https://doi.org/10.1109/AFGR.2002.1004170","url":null,"abstract":"Presents a modification of kernel-based Fisher discriminant analysis (FDA) for face detection. In a face detection problem, it is important to design a two-category classifier which can decide whether the given input sub-image is a face or not. There is a difficulty with training such two-category classifiers because the \"non-face\" class includes many images of different kinds of objects, and it is difficult to treat them all as a single class. Also, the dimension of the discriminant space constructed by the usual FDA is limited to one for two-category classification. To overcome these problems with the usual FDA, the discriminant criterion of the usual FDA is modified such that the covariance of the \"face\" class is minimized while the differences between the center of the \"face\" class and each training sample of the \"non-face\" class are maximized. By this modification, we can obtain a higher-dimensional discriminant space which is suitable for \"face/non-face\" classification. It is shown that the proposed method can outperform a support vector machine (SVM) by \"face/non-face\" classification experiments using the face images gathered from the available face databases and the many face images on the Web.","PeriodicalId":364299,"journal":{"name":"Proceedings of Fifth IEEE International Conference on Automatic Face Gesture Recognition","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134206262","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 24

Kernel Eigenfaces vs. Kernel Fisherfaces: Face recognition using kernel methods Kernel Eigenfaces vs. Kernel Fisherfaces:使用Kernel方法进行人脸识别

Proceedings of Fifth IEEE International Conference on Automatic Face Gesture Recognition Pub Date : 2002-05-20 DOI: 10.1109/FGR.2002.10001

Ming-Hsuan Yang

{"title":"Kernel Eigenfaces vs. Kernel Fisherfaces: Face recognition using kernel methods","authors":"Ming-Hsuan Yang","doi":"10.1109/FGR.2002.10001","DOIUrl":"https://doi.org/10.1109/FGR.2002.10001","url":null,"abstract":"Principal Component A nalysis and Fisher Linear Discriminant methods have demonstrated their success in fac edete ction, r ecognition and tr acking. The representations in these subspace methods are based on second order statistics of the image set, and do not address higher order statistical dependencies such as the relationships among three or more pixels. Recently Higher Order Statistics and Independent Component Analysis (ICA) have been used as informative representations for visual recognition. In this paper, we investigate the use of Kernel Principal Component Analysis and Kernel Fisher Linear Discriminant for learning low dimensional representations for face recognition, which we call Kernel Eigenface and Kernel Fisherface methods.While Eigenface and Fisherface methods aim to find projection directions based on second order correlation of samples, Kernel Eigenface and Kernel Fisherface methods provide generalizations which take higher order correlations into account. We compare the performance of kernel methods with classical algorithms such as Eigenface, Fisherface, ICA, and Support Vector Machine (SVM) within the context of appearance-based face recognition problem using two data sets where images vary in pose, scale, lighting and expression. Experimental results show that kernel methods provide better representations and achieve lower error rates for face recognition.","PeriodicalId":364299,"journal":{"name":"Proceedings of Fifth IEEE International Conference on Automatic Face Gesture Recognition","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130818124","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 834

Facial asymmetry quantification for expression invariant human identification 面部不对称量化用于表情不变人的识别

Proceedings of Fifth IEEE International Conference on Automatic Face Gesture Recognition Pub Date : 2002-05-20 DOI: 10.1109/AFGR.2002.1004156

Yanxi Liu, Karen L. Schmidt, J. Cohn, S. Mitra

引用次数: 154