6Applepolish
发表于 2025-3-28 17:08:44
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引起痛苦
发表于 2025-3-28 19:06:42
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
发表于 2025-3-28 23:45:55
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
发表于 2025-3-29 03:45:20
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小母马
发表于 2025-3-29 09:21:33
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
发表于 2025-3-29 12:47:44
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crucial
发表于 2025-3-29 19:19:16
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
发表于 2025-3-29 22:22:19
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
发表于 2025-3-30 00:24:56
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
reperfusion
发表于 2025-3-30 06:15:44
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