DeepGender: Occlusion and Low Resolution Robust Facial Gender Classification via Progressively Trained Convolutional Neural Networks with Attention
In this work, we have undertaken the task of occlusion and low-resolution robust facial gender classification. Inspired by the trainable attention model via deep architecture, and the fact that the periocular region is proven to be the most salient region for gender classification purposes, we are able to design a progressive convolutional neural network training paradigm to enforce the attention shift during the learning process. The hope is to enable the network to attend to particular high-profile regions (e.g. the periocular region) without the need to change the network architecture itself. The network benefits from this attention shift and becomes more robust towards occlusions and low-resolution degradations. With the progressively trained CNN models, we have achieved better gender classification results on the large-scale PCSO mugshot database with 400K images under occlusion and low-resolution settings, compared to the one undergone traditional training. In addition, our progressively trained network is sufficiently generalized so that it can be robust to occlusions of arbitrary types and at arbitrary locations, as well as low resolution.