3.8 Proceedings Paper

Face Detection with the Faster R-CNN

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IEEE
DOI: 10.1109/FG.2017.82

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  1. Office of the Director of National Intelligence (ODNI), Intelligence Advanced Research Projects Activity (IARPA) [2014-14071600010]

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While deep learning based methods for generic object detection have improved rapidly in the last two years, most approaches to face detection are still based on the R-CNN framework [11], leading to limited accuracy and processing speed. In this paper, we investigate applying the Faster RCNN [26], which has recently demonstrated impressive results on various object detection benchmarks, to face detection. By training a Faster R-CNN model on the large scale WIDER face dataset [34], we report state-of-the-art results on the WIDER test set as well as two other widely used face detection benchmarks, FDDB and the recently released IJB-A.

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