EndtoEnd Protocols and Performance Metrics For Unconstrained Face Recognition Face recognition algorithms have received substantial attention over the past decade resulting in significant performance improvements. Arguably, improvement can be attributed to the wide spread availability of large face training sets, GPU computing to train stateoftheart deep learning algorithms, and curation of challenging test sets that continue to push the stateoftheart. Traditionally, protocol design and algorithm evaluation have primarily focused on measuring performance of specific stages of the biometric pipeline (e.g., face detection, feature extraction, or recognition) and do not capture errors that may propagate from face input to identification output in an endtoend (E2E) manner. In this paper, we address this problem by expanding upon the novel openset E2E identification protocols created for the IARPA Janus program. In particular, we describe in detail the joint detection, tracking, clustering, and recognition protocols, introduce novel E2E performance metrics, and provide rigorous evaluation using the IARPA Janus Benchmark C (IJBC) and S (IJBS) datasets.

A Framework for RealTime FaceRecognition The advent and wide use of deeplearning technology has enabled tremendous advancements in the accuracy of face recognition under favourable conditions. Nonetheless, the reported nearperfect 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 endtoend framework for realtime videobased face recognition. This system detects, tracks and recognizes individuals from live video feed. The proposed system addresses three key challenges of videobased face recognition systems: endtoend computational complexity, in the wild recognition and multiperson 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.