Face recognition is a classic problem in the field of computer vision and popular due to its wide applications in real-world problems such as access control, identity verification, physical security, surveillance, etc. Recent progress in deep learning techniques and the access to large-scale face databases has led to a significant improvement of face recognition accuracy under constrained and semi-constrained scenarios. Deep neural networks are shown to surpass human performance on Labelled Face in the Wild (LFW), which consists of celebrity photos captured in the wild. This technology can be used to create a surveillance for the ingress systems with broad scope of application in multiple scenarios. The major ingress problems that will be address will include, but are not limited to: lack of automata, robust face normalization, discrimination, representation learning and the ambiguity of facial features caused by information loss. This paper discusses biometric ingress surveillance. With a brief review of the subject the paper presents an application in this domain that highlights the true potential of a surveillance system that can have face recognition as a biometric authentication to ingress at homes, offices, universities and other potential ingress systems.
