Multi-attribute Residual Network (MAResNet) for Soft-Biometrics Recognition in Surveillance Scenarios

In surveillance images, soft biometric attributes have been demonstrated to be quite effective in the problem of person re-identification. Many of these attributes can vary greatly, which has motivated a number of hand crafted features designed to recognize individual attributes. Although deep learning is generally useful in learning features appropriate for classification, it usually requires more data to train than what is available in most person re-identification databases. In this paper, we propose a residual network (MAResNet) deeper than current pedestrian attribute recognition, which we use to recognize multiple attributes simultaneously. The proposed network is both efficient and accurate. We recognize attributes at a rate of 271 FPS while simultaneously outperforming state of the art in attribute recognition on the PETA dataset.