Cascading Scene and Viewpoint Feature Learning for Pedestrian Gender Recognition

Pedestrian gender recognition plays an important role in smart city. To effectively improve the pedestrian gender recognition performance, a new method, called cascading scene and viewpoint feature learning (CSVFL), is proposed in this article. The novelty of the proposed CSVFL lies on the joint consideration of two crucial challenges in pedestrian gender recognition, namely, scene and viewpoint variation. For that, the proposed CSVFL starts with the scene transfer (ST) scheme, followed by the viewpoint adaptation (VA) scheme in a cascading manner. Specifically, the ST scheme exploits the key pedestrian segmentation network to extract the key pedestrian masks for the subsequent key pedestrian transfer generative adversarial network, with the goal of encouraging the input pedestrian image to have the similar style to the target scene while preserving the image details of the key pedestrian as much as possible. Afterward, the obtained scene-transferred pedestrian images are fed to train the deep feature learning network with the VA scheme, in which each neuron will be enabled/disabled for different viewpoints depending on whether it has contribution on the corresponding viewpoint. Extensive experiments conducted on the commonly used pedestrian attribute data sets have demonstrated that the proposed CSVFL approach outperforms multiple recently reported pedestrian gender recognition methods.