Dominant feature based convolutional neural network for faces in videos

Abstract

Standard face recognition modules are fabricated for general-purpose applications while few have been designed with speed in mind. This paper proposes an efficient architecture for face recognition in which two self-contained Convolutional Neural Networks (CNNs) are used to detect and recognize faces in regions containing a dense grouping of Features from Accelerated Segment Test (FAST). This configuration proves to be practical for videos as it is selective in its analysis of an input frame. City surveillance and public safety is a critical issue in smart cities and the deployment of Smart Video Surveillance systems is the need of the hour. Typically, the problem at hand will be person identification which is the association of a biometric trait with a particular human being. FAST key points can be generated and analyzed in near real-time and that data can be used to extract and process faces in the background. The CNNs were trained using a combination of datasets of labelled faces, videos and trivial objects. The results obtained upon analyzing the performance of the system on the ChokePoint dataset proved very insightful. This configuration leads to a very effective face recognition system.