Deep Parts Similarity Learning for Person Re-Identification

Measuring the appearance similarity in Person Re-Identification is a challenging task which not only requires the selection of discriminative visual descriptors but also their optimal combination. This paper presents a unified learning framework composed by Deep Convolutional Neural Networks to simultaneously and automatically learn the most salient features for each one of nine different body parts and their best weighting to form a person descriptor. Moreover, to cope with the cross-view variations, these have been coded in a Mahalanobis Matrix, in an adaptive process, also integrated into the learning framework, which takes advantage of the discriminative information given by the dataset labels to analyse the data structure. The effectiveness of the proposed approach, named Deep Parts Similarity Learning (DPSL), has been evaluated and compared with other state-of-the-art approaches over the challenging PRID2011 dataset.