Video-Based Person Authentication Using Face and Visual Speech.

This paper proposes a facial and visual speech feature extraction method for automatic person authentication in video. The method proposed in Viola and Jones (2006) is used to detect the face region. Face region is processed in YCbCr color space to determine the locations of the eyes. The system models the non-lip region of the face using a Gaussian distribution, and it is used to locate the center of the mouth. Facial and visual speech features are extracted using multiscale morphological erosion and dilation operations, respectively. The facial features are extracted relative to the locations of the eyes and visual speech features are extracted relative to the locations of the eyes and mouth. Auto-Associative Neural Network (AANN) and Support Vector Machines (SVMs) are analyzed for person authentication. AANN models are used to capture the distribution of facial and visual speech features of a subject. SVMs are used to construct the optimal separating hyperplane for facial and visual speech features. The evidence from face and visual speech modalities are combined using a weighting rule, and the result is used to accept or reject the identity claim of the subject. The performance of the system is evaluated for XM2VTS database. It is seen that the system achieves an Equal Error Rate (EER) of about 0.41% and 0.37% for 50 subjects using AANN and SVM, respectively. Finally, the performance of the AANN and SVM models for person authentication are compared. Experimental results show that the SVM gives better performance than the AANN model. [ABSTRACT FROM AUTHOR]