Titlebook: Biometric Recognition; 8th Chinese Conferen Zhenan Sun,Shiguan Shan,YiLong Yin Conference proceedings 2013 Springer International Publishin

6Applepolish

引起痛苦

0302-9743 ics, behavioral biometrics and other related topics, and contribute new ideas to research and development of reliable and practical solutions for biometric authentication.978-3-319-02960-3978-3-319-02961-0Series ISSN 0302-9743 Series E-ISSN 1611-3349

expdient

Normalization for Unconstrained Pose-Invariant 3D Face Recognitioncontains sufficient discriminant information. We propose to map the original 3D coordinates to a depth image using a specific resolution, hence, we can remain the original information in 3D space. 1) Posture correction, we propose 2 simple but effective methods to standardize a face model that is ap

箴言

小母马

Robust Face Recognition Based on Spatially-Weighted Sparse Codingng problem, robustness of face representation and recognition can be improved. In this paper, we propose to weight spatial locations based on their discriminabilities in sparse coding for robust face recognition. More specifically, we estimate the weights at image locations based on a class-specific

Diatribe

crucial

An Illumination Invariant Face Recognition Scheme to Combining Normalized Structural Descriptor withn variation, but its performance drop when illumination variation is large. We further analyze the normalized images under large illumination variation and we find that the illumination variation has not been removed thoroughly in these images. Structural similarity is one of image similarity metric

战胜

Shape Constraint and Multi-feature Fusion Particle Filter for Facial Feature Point Trackingever, they may easily fail to work, when there exists a change of face posture, expression or shelter. This paper presents a particle filter facial feature point tracking method that based on color and texture features and a shape constraint model. As the nostrils feature point area usually has non-

胖人手艺好

Robust Marginal Fisher Analysisalgorithm named Robust Marginal Fisher Analysis (RMFA) is proposed, which uses the recent advances on rank minimization. Marginal Fisher Analysis (MFA) is a supervised manifold learning method who perseveres the local manifold information. However, one major shortcoming of MFA is its brittleness wit

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