Deep Learning for Understanding Faces

Recent developments in deep convolutional neural networks (DCNNs) have shown impressive performance improvements on various object detection/recognition problems. This has been made possible due to the availability of large annotated data, a better understanding of the non-linear mapping between images and class labels as well as the affordability of powerful GPUs. These developments in deep learning have also improved the capabilities of machines in understanding faces and automatically executing the tasks of face detection, pose estimation, landmark localization, and face recognition from unconstrained images and videos. In this paper, we provide an overview of deep learning methods used for face recognition. We discuss different modules involved in designing an automatic face recognition system and the role of deep learning for each of them. Some open issues regarding DCNNs for face recognition problems are then discussed. The paper should prove valuable to scientists, engineers and end users working in the fields of face recognition, security, visual surveillance, and biometrics.