Face Recognition in Single Sample Per Person Fusing Multi-Scale Features Extraction and Virtual Sample Generation Methods

Abstract

Although face recognition has received a lot of attention and development in recent years, it is one of the research hotspots due to the low efficiency of Single Sample Per Person (SSPP) information in face recognition. In order to solve this problem, this article proposes a face recognition method based on virtual sample generation and multi-scale feature extraction. First, in order to increase the training sample information, a new NMF-MSB virtual sample generation method is proposed by combining the Non-negative Matrix Factorization (NMF) reconstruction strategy with Mirror transform(M), Sliding window(S), and Bit plane(B) sample extension methods. Second, a feature extraction method (named WPD-HOG-P) based on Wavelet Packet Decomposition, Histograms of Oriented Gradients, and image Pyramid is proposed. The proposed WPD-HOG-P method is beneficial to multi-scale facial image feature extraction. Finally, based on the extracted WPD-HOG-P features, the recognition model is established by using a grid search optimization support vector machine. Experimental results on ORL and FERET data sets show that the proposed method has higher recognition rates and lower computational complexity than the benchmark methods.