Abstract
There are many challenges for face based identity verification. It is one of fundamental topics in image processing and video analysis, and so on. A novel approach has been developed for facial identity verification based on a facial pose pool, which is constructed in an incremental clustering way to find both facial spatial information and orientation diversity. Bag of words is selected to extract image features from the facial pose pool in affine SIFT descriptor. The visual codebook is generated ink-means and Gaussian mixture model. Posterior pseudo probabilities are used to compute the similarities between each visual word and corresponding local features for image representation. Comparisons with some state-of-the-arts have highlighted the superior performance of the proposed method.
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