Titlebook: Audio- and Video-Based Biometric Person Authentication; Third International Josef Bigun,Fabrizio Smeraldi Conference proceedings 2001 Spri

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国家明智

FUSE

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Face Recognition Using Support Vector Machines with the Feature Set Extracted by Genetic Algorithms hair styles, and so on. This paper proposes a method of face recognition by using support vector machines with the feature set extracted by genetic algorithms. By selecting the feature set that has superior performance in recognizing faces, the use of unnecessary information of the faces can be avo

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Comparative Performance Evaluation of Gray-Scale and Color Information for Face Recognition Tasksrmation improves performance for detecting and locating eyes and faces, respectively, and that there is no significant difference in recognition accuracy between full color and gray-scale face imagery. Our experiments have also shown that the eigenvectors generated by the red channel lead to improve

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Face Recognition by Auto-associative Radial Basis Function Networko capture the substantial facial features and reduce computational complexity, we propose to use wavelet transform (WT) to decompose face images and choose the lowest resolution subband coefficients for face representation. Results indicate that our scheme yields accurate recognition on the widely u

Arthr-

Face Recognition Using Independent Component Analysis and Support Vector Machines ⋆ave demonstrated high generalization capabilities in many different tasks, including the object recognition problem. ICA is a feature extraction technique which can be considered a generalization of Principal Component Analysis (PCA). ICA has been mainly used on the problem of blind signal separatio

尽忠

A Comparison of Face/Non-face Classiffiers an evaluation protocol for face/non-face classification and provide experimental comparison of six algorithms. The overall best performing algorithms are the baseline template matching algorithms. Our results emphasize the importance of preprocessing.

CHIP

Using Mixture Covariance Matrices to Improve Face and Facial Expression Recognitionsof training samples for each pattern is significantly less than the dimension of the feature space. This statement implies that the sample group covariance matrices often used in the Gaussian maximum probability classifier are singular. A common solution to this problem is to assume that all groups