An unsupervised nonparametric approach is proposed to automatically extract representative face samples (exemplars) from a video sequence or an image set for multiple-shot face recognition. Motivated by a nonlinear dimensionality reduction algorithm called Isomap, we use local neighborhood information to approximate the geodesic distances between face images. A hierarchical agglomerative clustering (HAC) algorithm is then applied to group similar faces together based on the estimated geodesic distances which approximate their locations on the appearance manifold. We define the exemplars as cluster centers for template matching at the subsequent testing stage. The final recognition is the outcome of a majority voting scheme which combines the decisions from all the individual frames in the test set. Experimental results on a 40-subject video database demonstrate the effectiveness and flexibility of our proposed method
{"title":"Face recognition with image sets using hierarchically extracted exemplars from appearance manifolds","authors":"Wei-liang Fan, D. Yeung","doi":"10.1109/FGR.2006.47","DOIUrl":"https://doi.org/10.1109/FGR.2006.47","url":null,"abstract":"An unsupervised nonparametric approach is proposed to automatically extract representative face samples (exemplars) from a video sequence or an image set for multiple-shot face recognition. Motivated by a nonlinear dimensionality reduction algorithm called Isomap, we use local neighborhood information to approximate the geodesic distances between face images. A hierarchical agglomerative clustering (HAC) algorithm is then applied to group similar faces together based on the estimated geodesic distances which approximate their locations on the appearance manifold. We define the exemplars as cluster centers for template matching at the subsequent testing stage. The final recognition is the outcome of a majority voting scheme which combines the decisions from all the individual frames in the test set. Experimental results on a 40-subject video database demonstrate the effectiveness and flexibility of our proposed method","PeriodicalId":109260,"journal":{"name":"7th International Conference on Automatic Face and Gesture Recognition (FGR06)","volume":"91 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133884706","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}
Myung-Cheol Roh, W. Christmas, J. Kittler, Seong-Whan Lee
Player's gesture and action spotting in sports video is a key task in automatic analysis of the video material at a high level. In many sports views, the camera covers a large part of the sports arena, so that the area of player's region is small, and has large motion. These make the determination of the player's gestures and actions a challenging task. To overcome these problems, we propose a method based on curvature scale space templates of the player's silhouette. The use of curvature scale space makes the method robust to noise and our method is robust to significant shape corruption of a part of player's silhouette. We also propose a new recognition method which is robust to noisy sequence of posture and needs only a small amount of training data, which is essential characteristic for many practical applications
{"title":"Gesture Spotting in Low-Quality Video with Features Based on Curvature Scale Space","authors":"Myung-Cheol Roh, W. Christmas, J. Kittler, Seong-Whan Lee","doi":"10.1109/FGR.2006.59","DOIUrl":"https://doi.org/10.1109/FGR.2006.59","url":null,"abstract":"Player's gesture and action spotting in sports video is a key task in automatic analysis of the video material at a high level. In many sports views, the camera covers a large part of the sports arena, so that the area of player's region is small, and has large motion. These make the determination of the player's gestures and actions a challenging task. To overcome these problems, we propose a method based on curvature scale space templates of the player's silhouette. The use of curvature scale space makes the method robust to noise and our method is robust to significant shape corruption of a part of player's silhouette. We also propose a new recognition method which is robust to noisy sequence of posture and needs only a small amount of training data, which is essential characteristic for many practical applications","PeriodicalId":109260,"journal":{"name":"7th International Conference on Automatic Face and Gesture Recognition (FGR06)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127039867","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}
We present a robust frontal face detection method that enables the identification of face positions in images by combining the results of a low-resolution whole face and individual face parts classifiers. Our approach is to use face parts information and change the identification strategy based on the results from individual face parts classifiers. These classifiers were implemented based on AdaBoost. Moreover, we propose a novel method based on a decision tree to improve performance of face detectors for occluded faces. The proposed decision tree method distinguishes partially occluded faces based on the results from the individual classifies. Preliminarily experiments on a test sample set containing non-occluded faces and occluded faces indicated that our method achieved better results than conventional methods. Actual experimental results containing general images also showed better results
{"title":"Component-based robust face detection using AdaBoost and decision tree","authors":"K. Ichikawa, T. Mita, O. Hori","doi":"10.1109/FGR.2006.33","DOIUrl":"https://doi.org/10.1109/FGR.2006.33","url":null,"abstract":"We present a robust frontal face detection method that enables the identification of face positions in images by combining the results of a low-resolution whole face and individual face parts classifiers. Our approach is to use face parts information and change the identification strategy based on the results from individual face parts classifiers. These classifiers were implemented based on AdaBoost. Moreover, we propose a novel method based on a decision tree to improve performance of face detectors for occluded faces. The proposed decision tree method distinguishes partially occluded faces based on the results from the individual classifies. Preliminarily experiments on a test sample set containing non-occluded faces and occluded faces indicated that our method achieved better results than conventional methods. Actual experimental results containing general images also showed better results","PeriodicalId":109260,"journal":{"name":"7th International Conference on Automatic Face and Gesture Recognition (FGR06)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128168321","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}
This paper explores the extraction of features from color imagery for recognition tasks, especially face recognition. The well-known Gabor filter, which is typically defined as a complex function, has been extended to the hypercomplex (quaternion) domain. Several proposed modes of this extension are discussed, and a preferred formulation is selected. To quantify the effectiveness of this novel filter for color-based feature extraction, an elastic graph implementation for human face recognition has been extended to color images, and performance of the corresponding monochromatic and color recognition systems have been compared. Our experiments have shown an improvement of 3% to 17% in recognition accuracy over the analysis of monochromatic images using complex Gabor filters
{"title":"Color face recognition by hypercomplex Gabor analysis","authors":"Creed F. Jones, A. L. Abbott","doi":"10.1109/FGR.2006.30","DOIUrl":"https://doi.org/10.1109/FGR.2006.30","url":null,"abstract":"This paper explores the extraction of features from color imagery for recognition tasks, especially face recognition. The well-known Gabor filter, which is typically defined as a complex function, has been extended to the hypercomplex (quaternion) domain. Several proposed modes of this extension are discussed, and a preferred formulation is selected. To quantify the effectiveness of this novel filter for color-based feature extraction, an elastic graph implementation for human face recognition has been extended to color images, and performance of the corresponding monochromatic and color recognition systems have been compared. Our experiments have shown an improvement of 3% to 17% in recognition accuracy over the analysis of monochromatic images using complex Gabor filters","PeriodicalId":109260,"journal":{"name":"7th International Conference on Automatic Face and Gesture Recognition (FGR06)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132971287","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}
Masanobu Yamamoto, Humikazu Mitomi, F. Fujiwara, Taisuke Sato
This paper proposes a new approach for recognition of task-oriented actions based on stochastic context-free grammar (SCFG). Our attention puts on actions in the Japanese tea ceremony, where the action can be described by context-free grammar. Our aim is to recognize the action in the tea services. Existing SCFG approach consists of generating symbolic string, parsing it and recognition. The symbolic string often includes uncertainty. Therefore, the parsing process needs to recover the errors at the entry process. This paper proposes a segmentation method errorless as much as possible to segment an action into a string of finer actions. This method, based on an acceleration of the body motion, can produce the fine action corresponding to a terminal symbol with little error. After translating the sequence of fine actions into a set of symbolic strings, SCFG-based parsing of this set leaves small number of ones to be derived. Among the remaining strings, Bayesian classifier answers the action name with a maximum posterior probability. Giving one SCFG rule the multiple probabilities, one SCFG can recognize multiple actions
{"title":"Bayesian classification of task-oriented actions based on stochastic context-free grammar","authors":"Masanobu Yamamoto, Humikazu Mitomi, F. Fujiwara, Taisuke Sato","doi":"10.1109/FGR.2006.28","DOIUrl":"https://doi.org/10.1109/FGR.2006.28","url":null,"abstract":"This paper proposes a new approach for recognition of task-oriented actions based on stochastic context-free grammar (SCFG). Our attention puts on actions in the Japanese tea ceremony, where the action can be described by context-free grammar. Our aim is to recognize the action in the tea services. Existing SCFG approach consists of generating symbolic string, parsing it and recognition. The symbolic string often includes uncertainty. Therefore, the parsing process needs to recover the errors at the entry process. This paper proposes a segmentation method errorless as much as possible to segment an action into a string of finer actions. This method, based on an acceleration of the body motion, can produce the fine action corresponding to a terminal symbol with little error. After translating the sequence of fine actions into a set of symbolic strings, SCFG-based parsing of this set leaves small number of ones to be derived. Among the remaining strings, Bayesian classifier answers the action name with a maximum posterior probability. Giving one SCFG rule the multiple probabilities, one SCFG can recognize multiple actions","PeriodicalId":109260,"journal":{"name":"7th International Conference on Automatic Face and Gesture Recognition (FGR06)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129348123","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}
Sandhitsu R. Das, Robert C. Wilson, M. Lazarewicz, L. Finkel
We describe a methodology for classification of gait (walk, run, jog, etc.) and recognition of individuals based on gait using two successive stages of principal component analysis (PCA) on kinematic data. In psychophysical studies, we have found that observers are sensitive to specific "motion features" that characterize human gait. These spatiotemporal motion features closely correspond to the first few principal components (PC) of the kinematic data. The first few PCs provide a representation of an individual gait as trajectory along a low-dimensional manifold in PC space. A second stage of PCA captures variability in the shape of this manifold across individuals or gaits. This simple eigenspace based analysis is capable of accurate classification across subjects
{"title":"Gait recognition by two-stage principal component analysis","authors":"Sandhitsu R. Das, Robert C. Wilson, M. Lazarewicz, L. Finkel","doi":"10.1109/FGR.2006.56","DOIUrl":"https://doi.org/10.1109/FGR.2006.56","url":null,"abstract":"We describe a methodology for classification of gait (walk, run, jog, etc.) and recognition of individuals based on gait using two successive stages of principal component analysis (PCA) on kinematic data. In psychophysical studies, we have found that observers are sensitive to specific \"motion features\" that characterize human gait. These spatiotemporal motion features closely correspond to the first few principal components (PC) of the kinematic data. The first few PCs provide a representation of an individual gait as trajectory along a low-dimensional manifold in PC space. A second stage of PCA captures variability in the shape of this manifold across individuals or gaits. This simple eigenspace based analysis is capable of accurate classification across subjects","PeriodicalId":109260,"journal":{"name":"7th International Conference on Automatic Face and Gesture Recognition (FGR06)","volume":"106 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133975333","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}
In sign language recognition, one of the problems is to collect enough training data. Almost all of the statistical methods used in sign language recognition suffer from this problem. Inspired by the crossover of genetic algorithms, this paper presents a method to expand Chinese sign language (CSL) database through re-sampling from existing sign samples. Two original samples of the same sign are regarded as parents. They can reproduce their children by crossover. To verify the validity of the proposed method, some experiments are carried out on a vocabulary of 2435 gestures in Chinese sign language. Each gesture has 4 samples. Three samples are used to be the original generation. These three original samples and their offspring are used to construct the training set, and the remaining sample is used for test. The experimental results show that the new samples generated by the proposed method are effective
{"title":"Expanding Training Set for Chinese Sign Language Recognition","authors":"Chunli Wang, Xilin Chen, Wen Gao","doi":"10.1109/FGR.2006.39","DOIUrl":"https://doi.org/10.1109/FGR.2006.39","url":null,"abstract":"In sign language recognition, one of the problems is to collect enough training data. Almost all of the statistical methods used in sign language recognition suffer from this problem. Inspired by the crossover of genetic algorithms, this paper presents a method to expand Chinese sign language (CSL) database through re-sampling from existing sign samples. Two original samples of the same sign are regarded as parents. They can reproduce their children by crossover. To verify the validity of the proposed method, some experiments are carried out on a vocabulary of 2435 gestures in Chinese sign language. Each gesture has 4 samples. Three samples are used to be the original generation. These three original samples and their offspring are used to construct the training set, and the remaining sample is used for test. The experimental results show that the new samples generated by the proposed method are effective","PeriodicalId":109260,"journal":{"name":"7th International Conference on Automatic Face and Gesture Recognition (FGR06)","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123677269","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}
Hand detection and tracking play important roles in human computer interaction (HCI) applications, as well as surveillance. We propose a self initializing and self correcting tracking technique that is robust to different skin color, illumination and shadow irregularities. Self initialization is achieved from a detector that has relatively high false positive rate. The detected hands are then tracked backwards and forward in time using mean shift trackers initialized at each hand to find the candidate tracks for possible objects in the test sequence. Observed tracks are merged and weighed to find the real trajectories. Simple actions can be inferred extracting each object from the scene and interpreting their locations within each frame. Extraction is possible using the color histograms of the objects built during the detection phase. We apply the technique here to simple hand tracking with good results, without the need for training for skin color
{"title":"Self correcting tracking for articulated objects","authors":"M. Caglar, N. Lobo","doi":"10.1109/FGR.2006.100","DOIUrl":"https://doi.org/10.1109/FGR.2006.100","url":null,"abstract":"Hand detection and tracking play important roles in human computer interaction (HCI) applications, as well as surveillance. We propose a self initializing and self correcting tracking technique that is robust to different skin color, illumination and shadow irregularities. Self initialization is achieved from a detector that has relatively high false positive rate. The detected hands are then tracked backwards and forward in time using mean shift trackers initialized at each hand to find the candidate tracks for possible objects in the test sequence. Observed tracks are merged and weighed to find the real trajectories. Simple actions can be inferred extracting each object from the scene and interpreting their locations within each frame. Extraction is possible using the color histograms of the objects built during the detection phase. We apply the technique here to simple hand tracking with good results, without the need for training for skin color","PeriodicalId":109260,"journal":{"name":"7th International Conference on Automatic Face and Gesture Recognition (FGR06)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128160985","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}
This paper proposed a multi-template ASM algorithm addressing facial feature points detection under nonlinear shape variation of facial images with various kinds of expression. By adding texture information, adopting asymmetric sampling strategy for the feature points on outer contour of face, building multiple templates and integrating local ASM and global ASM, experimental results show that the proposed multi-template ASM algorithm outperforms traditional single template ASM
{"title":"Multi-template ASM Method for feature points detection of facial image with diverse expressions","authors":"Ying Li, J. Lai, P. Yuen","doi":"10.1109/FGR.2006.81","DOIUrl":"https://doi.org/10.1109/FGR.2006.81","url":null,"abstract":"This paper proposed a multi-template ASM algorithm addressing facial feature points detection under nonlinear shape variation of facial images with various kinds of expression. By adding texture information, adopting asymmetric sampling strategy for the feature points on outer contour of face, building multiple templates and integrating local ASM and global ASM, experimental results show that the proposed multi-template ASM algorithm outperforms traditional single template ASM","PeriodicalId":109260,"journal":{"name":"7th International Conference on Automatic Face and Gesture Recognition (FGR06)","volume":"26 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120873309","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}
This paper addresses nonlinear feature extraction and small sample size (S3) problems in face recognition. In sample feature space, the distribution of face images is nonlinear because of complex variations in pose, illumination and face expression. The performance of classical linear method, such as Fisher discriminant analysis (FDA), will degrade. To overcome pose and illumination problems, Shannon wavelet kernel is constructed and utilized for nonlinear feature extraction. Based on a modified Fisher criterion, simultaneous diagonalization technique is exploited to deal with S3 problem, which often occurs in FDA based methods. Shannon wavelet kernel based subspace Fisher discriminant (SWK-SFD) method is then developed in this paper. The proposed approach not only overcomes some drawbacks of existing FDA based algorithms, but also has good computational complexity. Two databases, namely FERET and CMU PIE face databases, are selected for evaluation. Comparing with the existing PDA-based methods, the proposed method gives superior results
{"title":"Face classification based on Shannon wavelet kernel and modified Fisher criterion","authors":"Wensheng Chen, P. Yuen, Jian Huang, J. Lai","doi":"10.1109/FGR.2006.41","DOIUrl":"https://doi.org/10.1109/FGR.2006.41","url":null,"abstract":"This paper addresses nonlinear feature extraction and small sample size (S3) problems in face recognition. In sample feature space, the distribution of face images is nonlinear because of complex variations in pose, illumination and face expression. The performance of classical linear method, such as Fisher discriminant analysis (FDA), will degrade. To overcome pose and illumination problems, Shannon wavelet kernel is constructed and utilized for nonlinear feature extraction. Based on a modified Fisher criterion, simultaneous diagonalization technique is exploited to deal with S3 problem, which often occurs in FDA based methods. Shannon wavelet kernel based subspace Fisher discriminant (SWK-SFD) method is then developed in this paper. The proposed approach not only overcomes some drawbacks of existing FDA based algorithms, but also has good computational complexity. Two databases, namely FERET and CMU PIE face databases, are selected for evaluation. Comparing with the existing PDA-based methods, the proposed method gives superior results","PeriodicalId":109260,"journal":{"name":"7th International Conference on Automatic Face and Gesture Recognition (FGR06)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122703490","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}
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