  Face detection is essential to facial analysis tasks such as facial
reenactment and face recognition. Both cascade face detectors and anchor-based
face detectors have translated shining demos into practice and received
intensive attention from the community. However, cascade face detectors often
suffer from a low detection accuracy, while anchor-based face detectors rely
heavily on very large networks pre-trained on large scale image classification
datasets such as ImageNet [1], which is not computationally efficient for both
training and deployment. In this paper, we devise an efficient anchor-based
cascade framework called anchor cascade. To improve the detection accuracy by
exploring contextual information, we further propose a context pyramid maxout
mechanism for anchor cascade. As a result, anchor cascade can train very
efficient face detection models with a high detection accuracy. Specifically,
comparing with a popular CNN-based cascade face detector MTCNN [2], our anchor
cascade face detector greatly improves the detection accuracy, e.g., from
0.9435 to 0.9704 at 1k false positives on FDDB, while it still runs in
comparable speed. Experimental results on two widely used face detection
benchmarks, FDDB and WIDER FACE, demonstrate the effectiveness of the proposed
framework.
