Uniface: A Unified Network for Face Detection and Recognition

Abstract

Typically, cropped and aligned face images are required as the input of a face recognition model. In contrast, popular object detectors based on deep convolutional network usually locate and classify objects simultaneously, which eliminates redundant computation. This work presents a single-network model called Uniface network for simultaneous face detection, landmark localization and recognition. We develop a feature sharing infrastructure for seamlessly integrate both the detection/localization module and the recognition module. To facilitate large-scale end-to-end training, we propose a method by encouraging top-level features of our model to mimic those of a well-trained single-task face recognition model. Comprehensive experiments on face detection, landmark localization and verification tasks demonstrate that the proposed network achieves competing performance in both face recognition benchmark (99.0% on LFW for a single model) and face detection benchmark (86.4% against 2000 false positives on FDDB for a single model).