Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004., 2004
Gabor feature has been widely recognized as better representation for face recognition in terms o... more Gabor feature has been widely recognized as better representation for face recognition in terms of rank-1 recognition rate. In this paper, we review the strength of Gabor feature for face recognition from the new angle of its robustness to mis-alignment using a novel quantificational evaluation method combining both the alignment precision and the recognition accuracy. Our experiments show that, compared with the gray-level intensity, Gabor feature is much more robust to image variation caused by the imprecision of facial feature localization, which further support the feasibility of Gabor representation.
This paper presents a novel face recognition method based on cascade Linear Discriminant Analysis... more This paper presents a novel face recognition method based on cascade Linear Discriminant Analysis (LDA) of the component-based face representation. In the proposed method, a face image is represented as four components with overlap at the neighboring area rather than a whole face patch. Firstly, LDA is conducted on the principal components of each component individually to extract component discriminant features. Then, these features are further concatenated to undergo another LDA to extract the final face descriptor, which actually have assigned different weights to different component features. Our experiments on the FERET face database have illustrated the effectiveness of the proposed method compared with the traditional Fisherface method both for face recognition and verification.
Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004., 2004
Information Fusion of multi-modal Biometrics has attracted much attention in the recent years. Ho... more Information Fusion of multi-modal Biometrics has attracted much attention in the recent years. However, this paper focuses on the information fusion in single modal, that is, the face Biometric. Two different representation methods, gray level intensity and Gabor feature, are exploited for fusion. We study the fusion problem in face recognition at both the face representation level and confidence level. At the representation level, both PCA feature fusion and LDA feature fusion are considered, while at the confidence level, sum rule and product rule are investigated. We show through experiments on FERET face database and our own face database that appropriate information fusion can improve the performance of both face recognition and verification. This suggests that gray level intensity and Gabor feature compensate for each other, based on which fusion is feasible.
In this paper, we present the rarely concerned curse of mis-alignment problem in face recognition... more In this paper, we present the rarely concerned curse of mis-alignment problem in face recognition, and propose a novel mis-alignment learning solution. Mis-alignment problem is firstly e mpirically investigated through systematically evaluating Fisherface' s sensitivity to misalignment on the FERET face database by perturbing the eye coordinates, which reveals that the imprecise localization of the facial landmarks abruptly degenerates the Fisherface system. We explicitly define this problem as curse of mis-alignment for highlighting its graveness. We then analyze the sources of curse of mis-alignment and group the possible solutions into three categories: invariant features, mis-alignment modeling, and alignment retuning. And then we propose a set of measurement combining the recognition rate with the alignment error distribution to evaluate the overall performance of specific face recognition approach with its robustness against the mis-alignment considered. Finally, a novel mis-alignment learning method, named E-Fisherface, is proposed to reinforce the recognizer to model the mis-alignment variations. Experimental results have impressively indicated the effectiveness of the proposed E-Fisherface to tackle the curse of misalignment problem.
International Conference on Pattern Recognition, 2004
Gabor feature has been widely recognized as better representation for face recognition in terms o... more Gabor feature has been widely recognized as better representation for face recognition in terms of rank-1 recognition rate. In this paper, we review the strength of Gabor feature for face recognition from the new angle of its robustness to mis-alignment using a novel quantificational evaluation method combining both the alignment precision and the recognition accuracy. Our experiments show that, compared with the gray-level intensity, Gabor feature is much more robust to image variation caused by the imprecision of facial feature localization, which further support the feasibility of Gabor representation.
IEEE International Conference on Automatic Face and Gesture Recognition, 2004
In this paper, we present the rarely concerned curse of mis-alignment problem in face recognition... more In this paper, we present the rarely concerned curse of mis-alignment problem in face recognition, and propose a novel mis-alignment learning solution. Mis-alignment problem is firstly empirically investigated through systematically evaluating Fisherface's sensitivity to mis-alignment on the FERET face database by perturbing the eye coordinates, which reveals that the imprecise localization of the facial landmarks abruptly degenerates the Fisherface system. We explicitly define this problem as curse of mis-alignment to highlight its graveness. We then analyze the sources of curse of mis-alignment and group the possible solutions into three categories: invariant features, mis-alignment modeling, and alignment retuning. And then we propose a set of measurement combining the recognition rate with the alignment error distribution to evaluate the overall performance of specific face recognition approach with its robustness against the mis-alignment considered. Finally, a novel mis-alignment learning method, named E-Fisherface, is proposed to reinforce the recognizer to model the mis-alignment variations. Experimental results have impressively indicated the effectiveness of the proposed E-Fisherface in tackling the curse of mis-alignment problem.
Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004., 2004
Gabor feature has been widely recognized as better representation for face recognition in terms o... more Gabor feature has been widely recognized as better representation for face recognition in terms of rank-1 recognition rate. In this paper, we review the strength of Gabor feature for face recognition from the new angle of its robustness to mis-alignment using a novel quantificational evaluation method combining both the alignment precision and the recognition accuracy. Our experiments show that, compared with the gray-level intensity, Gabor feature is much more robust to image variation caused by the imprecision of facial feature localization, which further support the feasibility of Gabor representation.
This paper presents a novel face recognition method based on cascade Linear Discriminant Analysis... more This paper presents a novel face recognition method based on cascade Linear Discriminant Analysis (LDA) of the component-based face representation. In the proposed method, a face image is represented as four components with overlap at the neighboring area rather than a whole face patch. Firstly, LDA is conducted on the principal components of each component individually to extract component discriminant features. Then, these features are further concatenated to undergo another LDA to extract the final face descriptor, which actually have assigned different weights to different component features. Our experiments on the FERET face database have illustrated the effectiveness of the proposed method compared with the traditional Fisherface method both for face recognition and verification.
Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004., 2004
Information Fusion of multi-modal Biometrics has attracted much attention in the recent years. Ho... more Information Fusion of multi-modal Biometrics has attracted much attention in the recent years. However, this paper focuses on the information fusion in single modal, that is, the face Biometric. Two different representation methods, gray level intensity and Gabor feature, are exploited for fusion. We study the fusion problem in face recognition at both the face representation level and confidence level. At the representation level, both PCA feature fusion and LDA feature fusion are considered, while at the confidence level, sum rule and product rule are investigated. We show through experiments on FERET face database and our own face database that appropriate information fusion can improve the performance of both face recognition and verification. This suggests that gray level intensity and Gabor feature compensate for each other, based on which fusion is feasible.
In this paper, we present the rarely concerned curse of mis-alignment problem in face recognition... more In this paper, we present the rarely concerned curse of mis-alignment problem in face recognition, and propose a novel mis-alignment learning solution. Mis-alignment problem is firstly e mpirically investigated through systematically evaluating Fisherface' s sensitivity to misalignment on the FERET face database by perturbing the eye coordinates, which reveals that the imprecise localization of the facial landmarks abruptly degenerates the Fisherface system. We explicitly define this problem as curse of mis-alignment for highlighting its graveness. We then analyze the sources of curse of mis-alignment and group the possible solutions into three categories: invariant features, mis-alignment modeling, and alignment retuning. And then we propose a set of measurement combining the recognition rate with the alignment error distribution to evaluate the overall performance of specific face recognition approach with its robustness against the mis-alignment considered. Finally, a novel mis-alignment learning method, named E-Fisherface, is proposed to reinforce the recognizer to model the mis-alignment variations. Experimental results have impressively indicated the effectiveness of the proposed E-Fisherface to tackle the curse of misalignment problem.
International Conference on Pattern Recognition, 2004
Gabor feature has been widely recognized as better representation for face recognition in terms o... more Gabor feature has been widely recognized as better representation for face recognition in terms of rank-1 recognition rate. In this paper, we review the strength of Gabor feature for face recognition from the new angle of its robustness to mis-alignment using a novel quantificational evaluation method combining both the alignment precision and the recognition accuracy. Our experiments show that, compared with the gray-level intensity, Gabor feature is much more robust to image variation caused by the imprecision of facial feature localization, which further support the feasibility of Gabor representation.
IEEE International Conference on Automatic Face and Gesture Recognition, 2004
In this paper, we present the rarely concerned curse of mis-alignment problem in face recognition... more In this paper, we present the rarely concerned curse of mis-alignment problem in face recognition, and propose a novel mis-alignment learning solution. Mis-alignment problem is firstly empirically investigated through systematically evaluating Fisherface's sensitivity to mis-alignment on the FERET face database by perturbing the eye coordinates, which reveals that the imprecise localization of the facial landmarks abruptly degenerates the Fisherface system. We explicitly define this problem as curse of mis-alignment to highlight its graveness. We then analyze the sources of curse of mis-alignment and group the possible solutions into three categories: invariant features, mis-alignment modeling, and alignment retuning. And then we propose a set of measurement combining the recognition rate with the alignment error distribution to evaluate the overall performance of specific face recognition approach with its robustness against the mis-alignment considered. Finally, a novel mis-alignment learning method, named E-Fisherface, is proposed to reinforce the recognizer to model the mis-alignment variations. Experimental results have impressively indicated the effectiveness of the proposed E-Fisherface in tackling the curse of mis-alignment problem.
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Papers by Yizheng Chang