Multi-feature sparse similar representation for person identification

Abstract

Person identification with a single feature (e.g., face recognition, speaker verification, person re-identification, etc.) has been studied extensively for many years, while few works focus on multi-feature person identification. Though promising performance has been achieved by only using the information of facial images, voice, or pedestrian appearance, it is still challenging to recognize a person with only a single feature in some situations (e.g., a person at a distance or occluded by other objects, and a partial person out of view). In this paper, we present a multi-feature sparse similar representation (MFSSR) method to effectively fuse face features, body features, and global image features for the task of person identification. In MFSSR, we designed a reconstructed deep spatial feature for representing the appearance of human body by using the spatial correlation coding of partial deep spatial features. Then we presented a multi-feature sparse similar representation model for jointly using different features, e.g., face, body, and the global image. Besides, considering that the coding coefficients associated with good samples but not outliers should be more similar among different features, we jointly represent different features by imposing a weighted ℓ1-norm distance regularization, instead of the conventional ℓ2-norm regularization, on the coefficients. Experimental results on several multi-feature person identification databases have clearly shown the superior performance of the proposed model.