FIRCNet: Feature-based image reconstruction with classification learning for person re-identification

Abstract

As an important task in computer vision, the person re-identification problem has two main approaches, identification and verification, in solving this problem. This paper proposed a network associating these two separate methods in training, including a reconstructive model. This design encompasses three core modules: a global feature extractor, a reconstructive decoder, and a classification network. Thus, the total loss function seamlessly integrates triplet loss, cross-entropy loss, and a reconstruction loss, optimizing the network for feature discriminability, accurate identity classification, and faithful image reconstruction concurrently. The global feature plays a pivotal role in both the training and testing phases, aiding in metric learning and ensuring a distinctive representation of identities. To train the decoder and the backbone network, which collectively forms the extractor, simultaneously, the reconstructive model as a part of a wide-range autoencoder can recover the original image from extracted features. Subsequently, the classification module classifies these features, assigning them to distinct person IDs, thereby aiding in precise identity recognition. Experimental results on three major re-id datasets give a demonstration of the notable improvement in the backbone network and the advantages of this approach compared to the state-of-the-art methods.