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Face Recognition Using Invariance with a Single Training Sample

Abstract: For the limits of memories and computing performance of current intelligent terminals, it is necessary to develop some strategies which can keep the balance of the accuracy and running time forface recognition. The purpose of the work in this paper is to find the invariant features of facial images and represent each subject with only one training sample for face recognition. We propose a two-layer hierarchical model, called invariance model, and its corresponding algorithms to keep the balance ofaccuracy, storage and running time. Especially, we take advantages of wavelet transformations andinvariant moments to obtain the key features as well as reduce dimensions of feature data based on thecognitive rules of human brains. Furthermore, we improve usual pooling methods, e.g. max pooling and average pooling, and propose the weighted pooling method to reduce dimensions with no effect onaccuracy, which let storage requirement and recognition time greatly decrease. The simulation results show that the proposed method does better than some typical and nearly-proposed algorithms in balancing the accuracy and running time.

Keywords: invariance model, single training sample, face recognition.

Qian Tian

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