Self-Supervised Capsule Network for Robust Face Recognition
Face recognition becomes one of the most adopted biometric technics due to its applications in intelligent surveillance, access control, border security, digital authentication, criminal investigation and human computer interaction. The development of Deep convolutional neural networks (CNNs) significantly improved accuracy of recognition even in unconstrained environments such as pose variations, illumination changes, facial expressions, occlusions and low-resolution images. Conventional CNNs mainly focus on local spatial features and so it has limited ability to preserve hierarchical association between facial components. Capsule Networks (CapsNets) overcome this by representing visual features as vector capsules with existence and geometric properties of objects. The self supervised learning of CapsNets enables feature learning from unlabelled images. This article presents a Self supervised Capsule Network (SS-CapsNet) integrates convolutional feature extraction, capsule based learning, dynamic routing and contrastive self supervised learning into a combined framework for robust face recognition. This approach simultaneously learns discriminative identity from large scale unlabelled dataset while preserves facial geometry. The SS-CapsNet provides improved robustness against pose variations, illumination changes, facial occlusions and image degradation.