  In this paper, we present a novel person reidentification (PRe-ID) system
that based on tensor feature representation and multilinear subspace learning.
Our approach utilizes pretrained CNNs for high-level feature extraction, along
with Local Maximal Occurrence (LOMO) and Gaussian Of Gaussian (GOG )
descriptors. Additionally, Cross-View Quadratic Discriminant Analysis (TXQDA)
algorithm is used for multilinear subspace learning, which models the data in a
tensor framework to enhance discriminative capabilities. Similarity measure
based on Mahalanobis distance is used for matching between training and test
pedestrian images. Experimental evaluations on VIPeR and PRID450s datasets
demonstrate the effectiveness of our method.
