End-to-End Spatial Transform Face Detection and Recognition

Abstract

BackgroundSeveral face detection and recognition methods have been proposed in the past decades that have excellent performance. The conventional face recognition pipeline comprises the following: (1) face detection, (2) face alignment, (3) feature extraction, and (4) similarity, which are independent of each other. The separate facial analysis stages lead to redundant model calculations, and are difficult for use in end-to-end training. MethodsIn this paper, we propose a novel end-to-end trainable convolutional network framework for face detection and recognition, in which a geometric transformation matrix is directly learned to align the faces rather than predicting the facial landmarks. In the training stage, our single CNN model is supervised only by face bounding boxes and personal identities, which are publicly available from WIDER FACE and CASIA-WebFace datasets. Our model is tested on Face Detection Dataset and Benchmark (FDDB) and Labeled Face in the Wild (LFW) datasets. ResultsThe results show 89.24% recall for face detection tasks and 98.63% accuracy for face recognition tasks.