A Framework for Real-Time Face-Recognition

Abstract

The advent and wide use of deep-learning technology has enabled tremendous advancements in the accuracy of face recognition under favourable conditions. Nonetheless, the reported near-perfect performance on classic benchmarks like lfw, does not include complications in unconstrained application. The research reported in this paper addresses some of the critical challenges of face recognition under adverse conditions. In this context, we introduce an end-to-end framework for real-time video-based face recognition. This system detects, tracks and recognizes individuals from live video feed. The proposed system addresses three key challenges of video-based face recognition systems: end-to-end computational complexity, in the wild recognition and multi-person recognition. We exploit sophisticated deep neural networks for face detection and facial feature extraction, while minimizing the computational overhead from the rest of the modules in the recognition pipeline. A comprehensive evaluation shows that the proposed system can effectively recognize faces under unconstrained conditions, at elevated frames per second rates.