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 R-CNN [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.
REF use the Faster R-CNN framework to improve the face detection performance.
Face Detection with the Faster R-CNN
2017 12th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2017)
2017 12th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2017)
Computer Science