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From Facial Parts Responses to Face Detection: A Deep Learning Approach
In this paper, we propose a novel deep convolutional network (DCN) that
achieves outstanding performance on FDDB, PASCAL Face, and AFW. Specifically,
our method achieves a high recall rate of 90.99% on the challenging FDDB
benchmark, outperforming the state-of-the-art method by a large margin of
2.91%. Importantly, we consider finding faces from a new perspective through
scoring facial parts responses by their spatial structure and arrangement. The
scoring mechanism is carefully formulated considering challenging cases where
faces are only partially visible. This consideration allows our network to
detect faces under severe occlusion and unconstrained pose variation, which are
the main difficulty and bottleneck of most existing face detection approaches.
We show that despite the use of DCN, our network can achieve practical runtime
speed.Comment: To appear in ICCV 201
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