Robust real-time video face recognition system for unconstrained environments

In everyday life, different biometric applications such as iris recognition and fingerprint recognition are used to identify individuals. The face is an important identifier for humans that can be used for surveillance, security purposes, access control, automated attendance, and so on. In this paper, a fully functional end-to-end facial recognition system is presented that can be applied in the real world. A simple face frontalness measure is proposed to filter out non-frontal faces that may result in false recognitions. To further reduce false positives, aggregation of prediction results across multiple frames is applied to form a single decision. Our model achieved a False Acceptance Rate (FAR) of 4.13% and False Rejection Rate (FRR) of 9.3% at the confidence threshold of 0.8 on the test dataset. Finally, the recognition result is displayed by a Web application. The system also records daily punch-in/punch-out times of employees and presents their monthly timesheet reports in the Web application. Our method achieves considerably lower false positive rates and runs at 18- 20 FPS in our testbed machine for a single camera.