F-DR Net:Face detection and recognition in One Net

Abstract

The face multi-task analysis is high-profile in recent years. Face detection and recognition are more challenging in one net. We present a new parallel network architecture for two face tasks in one net, achieving end-to-end face detection and recognition. Firstly, we train a better face detection network. Then, the selection of the shared layers has a signification impact on the result in speed and accuracy for recognition, so we determine the optimal shared layers by experiments. Finally, shared layers contains discriminative information for face recognition, and we put the recognition network under the shared layer of the detection network. We achieve parallel end-to-end face detection and recognition in one net, comprehensively evaluated this method on several face detection and recognition benchmark datasets, including the Labeled Faces in the Wild (LFW) and Face Detection Datasets and Benchmark (FDDB). We get better detection and recognition accuracy on LFW and FDDB, and achieve faster speed compared to other methods. Our results demonstrate the effectiveness of the proposed approach.