Multi-view gait recognition using NMF and 2DLDA

Abstract

View Transformation Model(VTM) is extensively employed in multi-view gait recognition. However, there still exists decline of matching accuracy among view transformation procedures. Particularly, the loss grows rapidly with the increase of the disparity of views. In the face of this difficulty, firstly, Non-negative Matrix Factorization(NMF) is introduced to obtain local structured features of human body to compensate accuracy loss. Moreover, 2D Linear Discriminant Analysis(2DLDA) is applied to improve classification ability by projecting features into a discriminant space. In the end, gait features, the Gait Energy Images(GEIs), is strengthened as 2D Enhanced GEI(2D-EGEI) by using the reconstruction of 2D Principal Component Analysis(2DPCA). Compared with the state-of-the-art, proposed method significantly outperforms the others. Furthermore, the comparisons of two deep learning methods is evaluated as well. Experimental outcomes show that the proposed method provides an alternative way to obtain the approximative outcomes compared with the deep learning methods.