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).

本文提出了一种使用深度神经网络技术实现的、适用于各种人脸检测和识别的解决方案，包括面部检测、特征定位和人脸验证等环节，并在许多测试数据集上达到了最新水平。