Multishot person reidentification using joint group sparse representation

Abstract

Sparse representation (SR) has shown great potential in multishot person reidentification. However, conventional SR methods usually encode each probe image individually, which ignores the prior knowledge shared by similar probe images with the same identity. Considering that probe images with the same identity are similar and can be complementary to each other, we propose a joint group sparse representation (JGSR) method to simultaneously encode a set of probe person images with the same identity. Additionally, we consider the fact that prior knowledge is also contained in the set of images with the same identity in the gallery. In JGSR, probe images with the same identity are coded on the same dictionary atoms and shared similar coefficients. Furthermore, the person reidentification performance is also improved by incorporating JGSR with k-means clustering and kernel local Fisher discriminant analysis into a unified framework. Extensive experiments on three publicly available iLIDS-VID, PRID 2011, and SAIVT-SoftBio multishot benchmark datasets were conducted and demonstrated the superior performance of the JGSR method in comparison with current state-of-the-art methods.