FA-RPN: Floating Region Proposals for Face Detection

Mahyar Najibi; Bharat Singh; Larry S. Davis

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arXiv · 1812.05586

FA-RPN: Floating Region Proposals for Face Detection

Mahyar Najibi·Bharat Singh·Larry S. Davis

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Abstract

We propose a novel approach for generating region proposals for performing face-detection. Instead of classifying anchor boxes using features from a pixel in the convolutional feature map, we adopt a pooling-based approach for generating region proposals. However, pooling hundreds of thousands of anchors which are evaluated for generating proposals becomes a computational bottleneck during inference. To this end, an efficient anchor placement strategy for reducing the number of anchor-boxes is proposed. We then show that proposals generated by our network (Floating Anchor Region Proposal Network, FA-RPN) are better than RPN for generating region proposals for face detection. We discuss several beneficial features of FA-RPN proposals like iterative refinement, placement of fractional anchors and changing anchors which can be enabled without making any changes to the trained model. Our face detector based on FA-RPN obtains 89.4% mAP with a ResNet-50 backbone on the WIDER dataset.

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BibTeX RIS

Mahyar Najibi, Bharat Singh, Larry S. Davis. 2018-12-13. FA-RPN: Floating Region Proposals for Face Detection. https://arxiv.org/abs/1812.05586

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