FaceHunter: A multi-task convolutional neural network based face detector

作者：

Highlights：

• The multi-task CNN is applied in the face detection task, and its validated to be very efficient. An adaptive pooling layer is integrated into the network to make it more flexible, and also the truncated SVD is introduced to compress the fully-connected layers.

• The RPN network is introduced to generate the proposals, which is directly performed on the convolutional feature maps. It shares the same features with multi-task CNN, so the proposal generating cost is very small.

• The proposed FaceHunter is evaluated on the AFW, FDDB and Pascal Faces respectively, and extensive experiments demonstrate its powerful performance against several state-of-the-art detectors.

摘要

Highlights•The multi-task CNN is applied in the face detection task, and its validated to be very efficient. An adaptive pooling layer is integrated into the network to make it more flexible, and also the truncated SVD is introduced to compress the fully-connected layers.•The RPN network is introduced to generate the proposals, which is directly performed on the convolutional feature maps. It shares the same features with multi-task CNN, so the proposal generating cost is very small.•The proposed FaceHunter is evaluated on the AFW, FDDB and Pascal Faces respectively, and extensive experiments demonstrate its powerful performance against several state-of-the-art detectors.

论文关键词：Face detection,Convolutional neural network,Multi-task,Adaptive pooling layer,Region proposal network

论文评审过程：Received 27 October 2015, Revised 19 April 2016, Accepted 19 April 2016, Available online 23 April 2016, Version of Record 29 September 2016.

论文官网地址：https://doi.org/10.1016/j.image.2016.04.004