Robust Partial Person Re-identification Based on Similarity-Guided Sparse Representation

In this paper, we study the problem of partial person re-identification (re-id). This problem is more difficult than general person re-identification because the body in probe image is not full. We propose a novel method, similarity-guided sparse representation (SG-SR), as a robust solution to improve the discrimination of the sparse coding. There are three main components in our method. In order to include multi-scale information, a dictionary consisting of features extracted from multi-scale patches is established in the first stage. A low rank constraint is then enforced on the dictionary based on the observation that its subspaces of each class should have low dimensions. After that, a classification model is built based on a novel similarity-guided sparse representation which can choose vectors that are more similar to the probe feature vector. The results show that our method outperforms existing partial person re-identification methods significantly and achieves state-of-the-art accuracy.