A Fast and Accurate System for Face Detection, Identification, and Verification
This work addresses the problem of fast and accurate face detection and recognition in unconstrained images and videos, which is crucial for security and surveillance applications, but it appears incremental as it builds on existing CNN methods.
The authors tackled unconstrained face detection and recognition by proposing a novel face detector (DPSSD) and a deep learning pipeline, achieving state-of-the-art performance on benchmarks like IJB-A, IJB-B, IJB-C, and CS5 with improved speed and accuracy for detecting faces, including tiny ones.
The availability of large annotated datasets and affordable computation power have led to impressive improvements in the performance of CNNs on various object detection and recognition benchmarks. These, along with a better understanding of deep learning methods, have also led to improved capabilities of machine understanding of faces. CNNs are able to detect faces, locate facial landmarks, estimate pose, and recognize faces in unconstrained images and videos. In this paper, we describe the details of a deep learning pipeline for unconstrained face identification and verification which achieves state-of-the-art performance on several benchmark datasets. We propose a novel face detector, Deep Pyramid Single Shot Face Detector (DPSSD), which is fast and capable of detecting faces with large scale variations (especially tiny faces). We give design details of the various modules involved in automatic face recognition: face detection, landmark localization and alignment, and face identification/verification. We provide evaluation results of the proposed face detector on challenging unconstrained face detection datasets. Then, we present experimental results for IARPA Janus Benchmarks A, B and C (IJB-A, IJB-B, IJB-C), and the Janus Challenge Set 5 (CS5).