Gabor filters based features, with their good properties of space-frequency localization and orientation selectivity, seem to be the most effective features for face recognition currently. In this paper, we propose a kind of weighted Gabor complex features which combining Gabor magnitude and phase features in unitary space. Its weights are determined according to recognition rates of magnitude and phase features. Meanwhile, subspace based algorithms, PCA and LDA, are generalized into unitary space, and a rarely used distance measure, unitary space cosine distance, is adopted for unitary subspace based recognition algorithms. Using the generalized subspace algorithms our proposed weighted Gabor complex features (WGCF) produce better recognition result than either Gabor magnitude or Gabor phase features. Experiments on FERET database show good results comparable to the best one reported in literature
{"title":"Weighted Gabor features in unitary space for face recognition","authors":"Yong Gao, Yangsheng Wang, Xinshan Zhu, Xuetao Feng, Xiaoxu Zhou","doi":"10.1109/FGR.2006.111","DOIUrl":"https://doi.org/10.1109/FGR.2006.111","url":null,"abstract":"Gabor filters based features, with their good properties of space-frequency localization and orientation selectivity, seem to be the most effective features for face recognition currently. In this paper, we propose a kind of weighted Gabor complex features which combining Gabor magnitude and phase features in unitary space. Its weights are determined according to recognition rates of magnitude and phase features. Meanwhile, subspace based algorithms, PCA and LDA, are generalized into unitary space, and a rarely used distance measure, unitary space cosine distance, is adopted for unitary subspace based recognition algorithms. Using the generalized subspace algorithms our proposed weighted Gabor complex features (WGCF) produce better recognition result than either Gabor magnitude or Gabor phase features. Experiments on FERET database show good results comparable to the best one reported in literature","PeriodicalId":109260,"journal":{"name":"7th International Conference on Automatic Face and Gesture Recognition (FGR06)","volume":"28 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":"130994261","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}
Active statistical models including active shape models and active appearance models are very powerful for face alignment. They are composed of two parts: the subspace model(s) and the search process. While these two parts are closely correlated, existing efforts treated them separately and had not considered how to optimize them overall. Another problem with the subspace model(s) is that the two kinds of parameters of subspaces (the number of components and the constraints on the components) are also treated separately. So they are not jointly optimized. To tackle these two problems, an unified subspace optimization method is proposed. This method is composed of two unification aspects: (I) unification of the statistical model and the search process: the subspace models are optimized according to the search procedure; (2) unification of the number of components and the constraints: the two kinds of parameters are modelled in an unified way, such that they can be optimized jointly. Experimental results demonstrate that our method can effectively find the optimal subspace model and significantly improve the performance
{"title":"Face Alignment with Unified Subspace Optimization of Active Statistical Models","authors":"Ming Zhao, Tat-Seng Chua","doi":"10.1109/FGR.2006.40","DOIUrl":"https://doi.org/10.1109/FGR.2006.40","url":null,"abstract":"Active statistical models including active shape models and active appearance models are very powerful for face alignment. They are composed of two parts: the subspace model(s) and the search process. While these two parts are closely correlated, existing efforts treated them separately and had not considered how to optimize them overall. Another problem with the subspace model(s) is that the two kinds of parameters of subspaces (the number of components and the constraints on the components) are also treated separately. So they are not jointly optimized. To tackle these two problems, an unified subspace optimization method is proposed. This method is composed of two unification aspects: (I) unification of the statistical model and the search process: the subspace models are optimized according to the search procedure; (2) unification of the number of components and the constraints: the two kinds of parameters are modelled in an unified way, such that they can be optimized jointly. Experimental results demonstrate that our method can effectively find the optimal subspace model and significantly improve the performance","PeriodicalId":109260,"journal":{"name":"7th International Conference on Automatic Face and Gesture Recognition (FGR06)","volume":"128 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":"129625743","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}
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}
Traditionally, motion estimation and segmentation have been performed mostly in the spatial domain, i.e., using the luminance information in the video sequence. Frequency domain representation offers an alternative, rich source of motion information, which has been used to a very limited extent in the past, and on relatively simple problems such as image registration. We review our work during the last few years on an approach to video motion analysis that combines spatial and Fourier domain information. We review our methods for (1) basic (translation and rotation) motion estimation and segmentation, for multiple moving objects, with constant as well as time varying velocities; and (2) more complicated motions, such as periodic motion, and periodic motion superposed on translation. The joint space analysis leads to more compact and computationally efficient solutions than existing techniques
{"title":"Joint spatial and frequency domain motion analysis","authors":"N. Ahuja, A. Briassouli","doi":"10.1109/FGR.2006.68","DOIUrl":"https://doi.org/10.1109/FGR.2006.68","url":null,"abstract":"Traditionally, motion estimation and segmentation have been performed mostly in the spatial domain, i.e., using the luminance information in the video sequence. Frequency domain representation offers an alternative, rich source of motion information, which has been used to a very limited extent in the past, and on relatively simple problems such as image registration. We review our work during the last few years on an approach to video motion analysis that combines spatial and Fourier domain information. We review our methods for (1) basic (translation and rotation) motion estimation and segmentation, for multiple moving objects, with constant as well as time varying velocities; and (2) more complicated motions, such as periodic motion, and periodic motion superposed on translation. The joint space analysis leads to more compact and computationally efficient solutions than existing techniques","PeriodicalId":109260,"journal":{"name":"7th International Conference on Automatic Face and Gesture Recognition (FGR06)","volume":"45 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":"126256636","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}
Non-linear subspaces derived using kernel methods have been found to be superior compared to linear subspaces in modeling or classification tasks of several visual phenomena. Such kernel methods include kernel PCA, kernel DA, kernel SVD and kernel QR. Since incremental computation algorithms for these methods do not exist yet, the practicality of these methods on large datasets or online video processing is minimal. We propose an approximate incremental kernel SVD algorithm for computer vision applications that require estimation of non-linear subspaces, specifically face recognition by matching image sets obtained through long-term observations or video recordings. We extend a well-known linear subspace updating algorithm to the nonlinear case by utilizing the kernel trick, and apply a reduced set construction method to produce sparse expressions for the derived subspace basis so as to maintain constant processing speed and memory usage. Experimental results demonstrate the effectiveness of the proposed method
{"title":"Incremental kernel SVD for face recognition with image sets","authors":"Tat-Jun Chin, K. Schindler, D. Suter","doi":"10.1109/FGR.2006.67","DOIUrl":"https://doi.org/10.1109/FGR.2006.67","url":null,"abstract":"Non-linear subspaces derived using kernel methods have been found to be superior compared to linear subspaces in modeling or classification tasks of several visual phenomena. Such kernel methods include kernel PCA, kernel DA, kernel SVD and kernel QR. Since incremental computation algorithms for these methods do not exist yet, the practicality of these methods on large datasets or online video processing is minimal. We propose an approximate incremental kernel SVD algorithm for computer vision applications that require estimation of non-linear subspaces, specifically face recognition by matching image sets obtained through long-term observations or video recordings. We extend a well-known linear subspace updating algorithm to the nonlinear case by utilizing the kernel trick, and apply a reduced set construction method to produce sparse expressions for the derived subspace basis so as to maintain constant processing speed and memory usage. Experimental results demonstrate the effectiveness of the proposed method","PeriodicalId":109260,"journal":{"name":"7th International Conference on Automatic Face and Gesture Recognition (FGR06)","volume":"25 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":"125083482","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 the PLAYBOT project, we aim at assisting disabled children at play. To this end, we are developing a semi autonomous robotic wheelchair. It is equipped with several visual sensors and a robotic manipulator and thus conveniently enhances the innate capabilities of a disabled child. In addition to a touch screen, the child may control the wheelchair using simple head movements. As control based on head posture requires reliable face detection and head pose recognition, we are in need of a robust technique that may effortlessly be tailored to individual users. In this paper, we present a multilinear classification algorithm for fast and reliable face detection. It trains within seconds and thus can easily be customized to the home environment of a disabled child. Subsequent head pose recognition is done using support vector machines. Experimental results show that this two stage approach to head pose-based robotic wheelchair control performs fast and very robust
{"title":"Fast learning for customizable head pose recognition in robotic wheelchair control","authors":"C. Bauckhage, Thomas Käster, Andrei M. Rotenstein","doi":"10.1109/FGR.2006.52","DOIUrl":"https://doi.org/10.1109/FGR.2006.52","url":null,"abstract":"In the PLAYBOT project, we aim at assisting disabled children at play. To this end, we are developing a semi autonomous robotic wheelchair. It is equipped with several visual sensors and a robotic manipulator and thus conveniently enhances the innate capabilities of a disabled child. In addition to a touch screen, the child may control the wheelchair using simple head movements. As control based on head posture requires reliable face detection and head pose recognition, we are in need of a robust technique that may effortlessly be tailored to individual users. In this paper, we present a multilinear classification algorithm for fast and reliable face detection. It trains within seconds and thus can easily be customized to the home environment of a disabled child. Subsequent head pose recognition is done using support vector machines. Experimental results show that this two stage approach to head pose-based robotic wheelchair control performs fast and very robust","PeriodicalId":109260,"journal":{"name":"7th International Conference on Automatic Face and Gesture Recognition (FGR06)","volume":"39 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":"115855519","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}
Yuhya Okada, Masataka Ozu, T. Sakurai, Mitsuharu Inaba, S. Akamatsu
This paper describes an attempt to transform a 3D model of a person's face to produce an intended change of impression. 3D shape and surface texture of faces are represented by high-dimensional vectors automatically extracted from the 3D data captured by a range finder, and variations among a set of faces are coded by applying principal component analysis. The relationship between the coded representation and the attribute of faces along a given impression dimension is analyzed to obtain an impression transfer vector. Here, we propose a method using this impression transfer vector to manipulate 3D faces in order to transform impressions. Experimental results on transformation of gender impressions confirmed the superiority of manipulating the 3D information of faces over a previous approach using only 2D face information
{"title":"Automatic impression transformation of faces in 3D shape - a perceptual comparison with processing on 2D images","authors":"Yuhya Okada, Masataka Ozu, T. Sakurai, Mitsuharu Inaba, S. Akamatsu","doi":"10.1109/FGR.2006.26","DOIUrl":"https://doi.org/10.1109/FGR.2006.26","url":null,"abstract":"This paper describes an attempt to transform a 3D model of a person's face to produce an intended change of impression. 3D shape and surface texture of faces are represented by high-dimensional vectors automatically extracted from the 3D data captured by a range finder, and variations among a set of faces are coded by applying principal component analysis. The relationship between the coded representation and the attribute of faces along a given impression dimension is analyzed to obtain an impression transfer vector. Here, we propose a method using this impression transfer vector to manipulate 3D faces in order to transform impressions. Experimental results on transformation of gender impressions confirmed the superiority of manipulating the 3D information of faces over a previous approach using only 2D face information","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":"115652068","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}
Estimating 3D head poses accurately in low resolution video is a challenging vision task because it is difficult to find continuous one-to-one mapping from person-independent low resolution visual representation to head pose parameters. We propose to track head poses by modeling the shape-free facial textures acquired from the video with subspace learning techniques. In particular, we propose to model the facial appearance variations online by incremental weighted PCA subspace with forgetting mechanism, and we do the tracking in an annealed particle filtering framework. Experiments show that, the tracking accuracy of our approach outperforms past visual face tracking algorithms especially in low resolution videos
{"title":"Accurate Head Pose Tracking in Low Resolution Video","authors":"J. Tu, Thomas S. Huang, Hai Tao","doi":"10.1109/FGR.2006.19","DOIUrl":"https://doi.org/10.1109/FGR.2006.19","url":null,"abstract":"Estimating 3D head poses accurately in low resolution video is a challenging vision task because it is difficult to find continuous one-to-one mapping from person-independent low resolution visual representation to head pose parameters. We propose to track head poses by modeling the shape-free facial textures acquired from the video with subspace learning techniques. In particular, we propose to model the facial appearance variations online by incremental weighted PCA subspace with forgetting mechanism, and we do the tracking in an annealed particle filtering framework. Experiments show that, the tracking accuracy of our approach outperforms past visual face tracking algorithms especially in low resolution videos","PeriodicalId":109260,"journal":{"name":"7th International Conference on Automatic Face and Gesture Recognition (FGR06)","volume":"3 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":"129587071","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 this paper, a robust 3D dance posture recognition system using two cameras is proposed. A pair of wide-baseline video cameras with approximately orthogonal looking directions is used to reduce pose recognition ambiguities. Silhouettes extracted from these two views are represented using Gaussian mixture models (GMM) and used as features for recognition. Relevance vector machine (RVM) is deployed for robust pose recognition. The proposed system is trained using synthesized silhouettes created using animation software and motion capture data. The experimental results on synthetic and real images illustrate that the proposed approach can recognize 3D postures effectively. In addition, the system is easy to set up without any need of precise camera calibration
{"title":"Dance posture recognition using wide-baseline orthogonal stereo cameras","authors":"Feng Guo, G. Qian","doi":"10.1109/FGR.2006.35","DOIUrl":"https://doi.org/10.1109/FGR.2006.35","url":null,"abstract":"In this paper, a robust 3D dance posture recognition system using two cameras is proposed. A pair of wide-baseline video cameras with approximately orthogonal looking directions is used to reduce pose recognition ambiguities. Silhouettes extracted from these two views are represented using Gaussian mixture models (GMM) and used as features for recognition. Relevance vector machine (RVM) is deployed for robust pose recognition. The proposed system is trained using synthesized silhouettes created using animation software and motion capture data. The experimental results on synthetic and real images illustrate that the proposed approach can recognize 3D postures effectively. In addition, the system is easy to set up without any need of precise camera calibration","PeriodicalId":109260,"journal":{"name":"7th International Conference on Automatic Face and Gesture Recognition (FGR06)","volume":"18 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":"115023365","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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