Single Sample Face Identification Based on Generative Score Space

Abstract

Abstract:

In this paper, a generativescorespace model was proposed, based on which a face recognition approach was derived. First, the proposed approach designed a probabilistic generative model for face representation, which effectively combined the flexibility of partsbased paradigm with the robustness of sparse component analysis. Then, a score function (i.e. feature mapping) was derived based on the model. Besides, a similarity measure was constructed for single sample face identification, which is essentially the function over observed variables, hidden variables and model parameters. The proposed approach was evaluated on two standard face databases to validate its effectiveness.

Key words: generative score space, single sample face identification, probabilistic similarity measure

CLC Number:

TP 391.4

WANG Bin1,LIU Yuncai2,MAO Hongwei1. Single Sample Face Identification Based on Generative Score Space[J]. Journal of Shanghai Jiaotong University, 2017, 51(2): 202-.

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