The Experts below are selected from a list of 125484 Experts worldwide ranked by ideXlab platform
Marios Savvides - One of the best experts on this subject based on the ideXlab platform.
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human face identification from video based on frequency Domain asymmetry Representation using hidden markov models
ACM Multimedia, 2006Co-Authors: Sinjini Mitra, Marios Savvides, B Vijaya V K KumarAbstract:In this paper we introduce a novel human face identification scheme from video data based on a frequency Domain Representation of facial asymmetry. A Hidden Markov Model (HMM) is used to learn the temporal dynamics of the training video sequences of each subject and classification of the test video sequences is performed using the likelihood scores obtained from the HMMs. We apply this method to a video database containing 55 subjects showing extreme expression variations and demonstrate that the HMM-based method performs much better than identification based on the still images using an Individual PCA (IPCA) classifier, achieving more than 30% improvement.
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face identification using novel frequency Domain Representation of facial asymmetry
IEEE Transactions on Information Forensics and Security, 2006Co-Authors: Sinjini Mitra, Marios Savvides, B Vijaya V K KumarAbstract:Face recognition is a challenging task. This paper introduces a novel set of biometrics, defined in the frequency Domain and representing a form of "facial asymmetry." A comparison with existing spatial asymmetry measures suggests that the frequency-Domain Representation provides an efficient approach for performing human identification in the presence of severe expressions and for expression classification. Error rates of less than 5% are observed for human identification and around 25% for expression classification on a database of 55 individuals. Feature analysis indicates that asymmetry of the different face parts helps in these two apparently conflicting classification problems. An interesting connection between asymmetry and the Fourier Domain phase spectra is then established. Finally, a compact one-bit frequency-Domain Representation of asymmetry is introduced, and a simplistic Hamming distance classifier is shown to be more efficient than traditional classifiers from storage and the computation point of view, while producing equivalent human identification results. In addition, the application of these compact measures to verification and a statistical analysis are presented
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improved human face identification using frequency Domain Representation of facial asymmetry
International Conference on Acoustics Speech and Signal Processing, 2006Co-Authors: Sinjini Mitra, Marios SavvidesAbstract:This paper explores the role of facial asymmetry in identification tasks using a frequency Domain Representation. Satisfactory results are obtained for two different tasks, namely, human identification under extreme expression variations and expression classification, using a PCA-type classifier which establishes the robustness of these measures to intra-personal distortions. We next demonstrate that it is possible to even improve upon these results by simple means. In particular, we use two methods, namely, feature set combination and statistical resampling methods like bagging, which attains perfect classification results (0% error rate) in some cases. Both these methods require very few additional resources in terms of computing power, hence they are useful for practical applications as well.
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using feature combination and statistical resampling for accurate face recognition based on frequency Domain Representation of facial asymmetry
International Conference on Automatic Face and Gesture Recognition, 2006Co-Authors: Sinjini Mitra, Marios SavvidesAbstract:This paper explores the efficiency of facial asymmetry in face identification tasks using a frequency Domain Representation. Satisfactory results are obtained for two different tasks, namely, human identification under extreme expression variations and expression classification, using a PCA-type classifier on a database with 55 individuals, which establishes the robustness of these measures to intra-personal distortions. Furthermore, we demonstrate that it is possible to improve upon these results significantly by simple means such as feature set combination and statistical resampling methods like bagging and random subspace method (RSM) using the same PCA-type base classifier. This even succeeds in attaining perfect classification results with 100% accuracy in some cases. Moreover, both these methods require few additional resources (computing time and power), hence they are useful for practical applications as well and help establish the effectiveness of frequency Domain Representation of facial asymmetry in automatic identification tasks
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analyzing asymmetry biometric in the frequency Domain for face recognition
International Conference on Acoustics Speech and Signal Processing, 2005Co-Authors: Sinjini Mitra, Marios SavvidesAbstract:The paper introduces a novel set of facial biometrics based on quantified facial asymmetry measures in the frequency Domain. In particular, we show that these biometrics work well for images showing expression variations. A comparison of the recognition rates with those obtained from spatial Domain asymmetry measures based on raw intensity values suggests that the frequency Domain Representation is more robust to intra-personal distortions and, indeed, provides an efficient approach for performing classification or recognition. The role of asymmetry of the different regions (e.g., eyes, mouth, nose) of the face is investigated to determine which regions provide the maximum discrimination among individuals in the presence of different expressions for better classification results in such a scenario.
Sinjini Mitra - One of the best experts on this subject based on the ideXlab platform.
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human face identification from video based on frequency Domain asymmetry Representation using hidden markov models
ACM Multimedia, 2006Co-Authors: Sinjini Mitra, Marios Savvides, B Vijaya V K KumarAbstract:In this paper we introduce a novel human face identification scheme from video data based on a frequency Domain Representation of facial asymmetry. A Hidden Markov Model (HMM) is used to learn the temporal dynamics of the training video sequences of each subject and classification of the test video sequences is performed using the likelihood scores obtained from the HMMs. We apply this method to a video database containing 55 subjects showing extreme expression variations and demonstrate that the HMM-based method performs much better than identification based on the still images using an Individual PCA (IPCA) classifier, achieving more than 30% improvement.
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face identification using novel frequency Domain Representation of facial asymmetry
IEEE Transactions on Information Forensics and Security, 2006Co-Authors: Sinjini Mitra, Marios Savvides, B Vijaya V K KumarAbstract:Face recognition is a challenging task. This paper introduces a novel set of biometrics, defined in the frequency Domain and representing a form of "facial asymmetry." A comparison with existing spatial asymmetry measures suggests that the frequency-Domain Representation provides an efficient approach for performing human identification in the presence of severe expressions and for expression classification. Error rates of less than 5% are observed for human identification and around 25% for expression classification on a database of 55 individuals. Feature analysis indicates that asymmetry of the different face parts helps in these two apparently conflicting classification problems. An interesting connection between asymmetry and the Fourier Domain phase spectra is then established. Finally, a compact one-bit frequency-Domain Representation of asymmetry is introduced, and a simplistic Hamming distance classifier is shown to be more efficient than traditional classifiers from storage and the computation point of view, while producing equivalent human identification results. In addition, the application of these compact measures to verification and a statistical analysis are presented
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improved human face identification using frequency Domain Representation of facial asymmetry
International Conference on Acoustics Speech and Signal Processing, 2006Co-Authors: Sinjini Mitra, Marios SavvidesAbstract:This paper explores the role of facial asymmetry in identification tasks using a frequency Domain Representation. Satisfactory results are obtained for two different tasks, namely, human identification under extreme expression variations and expression classification, using a PCA-type classifier which establishes the robustness of these measures to intra-personal distortions. We next demonstrate that it is possible to even improve upon these results by simple means. In particular, we use two methods, namely, feature set combination and statistical resampling methods like bagging, which attains perfect classification results (0% error rate) in some cases. Both these methods require very few additional resources in terms of computing power, hence they are useful for practical applications as well.
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using feature combination and statistical resampling for accurate face recognition based on frequency Domain Representation of facial asymmetry
International Conference on Automatic Face and Gesture Recognition, 2006Co-Authors: Sinjini Mitra, Marios SavvidesAbstract:This paper explores the efficiency of facial asymmetry in face identification tasks using a frequency Domain Representation. Satisfactory results are obtained for two different tasks, namely, human identification under extreme expression variations and expression classification, using a PCA-type classifier on a database with 55 individuals, which establishes the robustness of these measures to intra-personal distortions. Furthermore, we demonstrate that it is possible to improve upon these results significantly by simple means such as feature set combination and statistical resampling methods like bagging and random subspace method (RSM) using the same PCA-type base classifier. This even succeeds in attaining perfect classification results with 100% accuracy in some cases. Moreover, both these methods require few additional resources (computing time and power), hence they are useful for practical applications as well and help establish the effectiveness of frequency Domain Representation of facial asymmetry in automatic identification tasks
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analyzing asymmetry biometric in the frequency Domain for face recognition
International Conference on Acoustics Speech and Signal Processing, 2005Co-Authors: Sinjini Mitra, Marios SavvidesAbstract:The paper introduces a novel set of facial biometrics based on quantified facial asymmetry measures in the frequency Domain. In particular, we show that these biometrics work well for images showing expression variations. A comparison of the recognition rates with those obtained from spatial Domain asymmetry measures based on raw intensity values suggests that the frequency Domain Representation is more robust to intra-personal distortions and, indeed, provides an efficient approach for performing classification or recognition. The role of asymmetry of the different regions (e.g., eyes, mouth, nose) of the face is investigated to determine which regions provide the maximum discrimination among individuals in the presence of different expressions for better classification results in such a scenario.
B Vijaya V K Kumar - One of the best experts on this subject based on the ideXlab platform.
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human face identification from video based on frequency Domain asymmetry Representation using hidden markov models
ACM Multimedia, 2006Co-Authors: Sinjini Mitra, Marios Savvides, B Vijaya V K KumarAbstract:In this paper we introduce a novel human face identification scheme from video data based on a frequency Domain Representation of facial asymmetry. A Hidden Markov Model (HMM) is used to learn the temporal dynamics of the training video sequences of each subject and classification of the test video sequences is performed using the likelihood scores obtained from the HMMs. We apply this method to a video database containing 55 subjects showing extreme expression variations and demonstrate that the HMM-based method performs much better than identification based on the still images using an Individual PCA (IPCA) classifier, achieving more than 30% improvement.
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face identification using novel frequency Domain Representation of facial asymmetry
IEEE Transactions on Information Forensics and Security, 2006Co-Authors: Sinjini Mitra, Marios Savvides, B Vijaya V K KumarAbstract:Face recognition is a challenging task. This paper introduces a novel set of biometrics, defined in the frequency Domain and representing a form of "facial asymmetry." A comparison with existing spatial asymmetry measures suggests that the frequency-Domain Representation provides an efficient approach for performing human identification in the presence of severe expressions and for expression classification. Error rates of less than 5% are observed for human identification and around 25% for expression classification on a database of 55 individuals. Feature analysis indicates that asymmetry of the different face parts helps in these two apparently conflicting classification problems. An interesting connection between asymmetry and the Fourier Domain phase spectra is then established. Finally, a compact one-bit frequency-Domain Representation of asymmetry is introduced, and a simplistic Hamming distance classifier is shown to be more efficient than traditional classifiers from storage and the computation point of view, while producing equivalent human identification results. In addition, the application of these compact measures to verification and a statistical analysis are presented
Marc Levoy - One of the best experts on this subject based on the ideXlab platform.
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frequency Domain volume rendering
International Conference on Computer Graphics and Interactive Techniques, 1993Co-Authors: Takashi Totsuka, Marc LevoyAbstract:The Fourier projection-slice theorem allos projections of volume data to be generated in O(nsquare log n) time for a volumbe of size ncube. The method operates by extracting and inverse Fourier transforming 2D slices from a 3D frequency Domain Representation of the volume. Unfortunately, these projections do not exhibit the occlusion that is characteristic of conventional volume renderings. We present a new frequency Domain volume rendering algorithm that replaces much of the missing depth and shape cues by performing shading calculations in the frequency Domain during slice extraction. In particular, we demonstrate frequency Domain methods for computing linear or nonlinear depth cueing and directional diffuse reflection. The resulting images can be generated an order of magnitude faster than volume renderings and may be more useful for many applications.
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volume rendering using the fourier projection slice theorem
Graphics Interface, 1992Co-Authors: Marc LevoyAbstract:The Fourier projection-slice theorem states that the inverse transform of a slice extracted from the frequency Domain Representation of a volume yields a projection of the volume in a direction perpendicular to the slice. This theorem allows the generation of attenuation-only renderings of volume data in O (N 2 log N) time for a volume of size N . In this paper, we show how more realistic renderings can be generated using a class of shading models whose terms are Fourier projections. Models are derived for rendering depth cueing by linear attenuation of variable energy emitters and for rendering directional shading by Lambertian reflection with hemispherical illumination. While the resulting images do not exhibit the occlusion that is characteristic of conventional volume rendering, they provide sufficient depth and shape cues to give a strong illusion that occlusion exists.
Dibyendu Nandy - One of the best experts on this subject based on the ideXlab platform.
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a volumetric iconic frequency Domain Representation for objects with application for pose invariant face recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence, 1998Co-Authors: J Benarie, Dibyendu NandyAbstract:A novel method for representing 3D objects that unifies viewer and model centered object Representations is presented. A unified 3D frequency-Domain Representation, called volumetric frequency Representation (VFR), encapsulates both the spatial structure of the object and a continuum of its views in the same data structure. The frequency-Domain image of an object viewed from any direction can be directly extracted employing an extension of the projection slice theorem, where each Fourier-transformed view is a planar slice of the volumetric frequency Representation. The VFR is employed for pose-invariant recognition of complex objects, such as faces. The recognition and pose estimation is based on an efficient matching algorithm in a four-dimensional Fourier space. Experimental examples of pose estimation and recognition of faces in various poses are also presented.
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Using the Fourier slice theorem for Representation of object views and models with application to face recognition
Proceedings of International Conference on Image Processing, 1997Co-Authors: Dibyendu Nandy, J. Ben-arieAbstract:A novel method unifying viewer and model centered approaches for representing structurally complex 3-D objects like human faces is presented. The unified 3-D frequency-Domain Representation (called volumetric/iconic spectral signatures-V/ISS) encapsulates both the spatial structure of the object and a continuum of the projection slice theorem is used to directly extract the frequency-Domain image of an object as viewed from any direction. Each such Fourier-transformed view is a planar slice of the volumetric frequency Representation. The V/ISS Representation is employed for pose-invariant recognition of complex objects such as faces. The recognition and pose estimation is based on an efficient matching algorithm in a four dimensional Fourier space. Experimental examples of pose estimation and recognition of faces are presented.