Nonnegative Sparse Probabilistic Estimation for Single Sample Face Recognition

Author:

Zhao Shuhuan1ORCID

Affiliation:

1. College of Electronic and Information Engineering, Hebei University, Baoding 071000, P. R. China

Abstract

Face recognition (FR) is a hotspot in pattern recognition and image processing for its wide applications in real life. One of the most challenging problems in FR is single sample face recognition (SSFR). In this paper, we proposed a novel algorithm based on nonnegative sparse representation, collaborative presentation, and probabilistic graph estimation to address SSFR. The proposed algorithm is named as Nonnegative Sparse Probabilistic Estimation (NNSPE). To extract the variation information from the generic training set, we first select some neighbor samples from the generic training set for each sample in the gallery set and the generic training set can be partitioned into some reference subsets. To make more meaningful reconstruction, the proposed method adopts nonnegative sparse representation to reconstruct training samples, and according to the reconstruction coefficients, NNSPE computes the probabilistic label estimation for the samples of the generic training set. Then, for a given test sample, collaborative representation (CR) is used to acquire an adaptive variation subset. Finally, the NNSPE classifies the test sample with the adaptive variation subset and probabilistic label estimation. The experiments on the AR and PIE verify the effectiveness of the proposed method both in recognition rates and time cost.

Funder

Young Fund of Hebei Education Department

Doctoral Start-up Foundation of Hebei Univeristy

Hebei Machine Vision Engineering Technology Research Center Open Fund

Publisher

World Scientific Pub Co Pte Lt

Subject

Artificial Intelligence,Computer Vision and Pattern Recognition,Software

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. A Joint Mapping and Synthesis Approach for Multiview Facial Expression Recognition;International Journal of Pattern Recognition and Artificial Intelligence;2021-04-01

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