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Face Detection with the Faster R-CNN | Synapse

May 1, 2017

Face Detection with the Faster R-CNN

Key Points

This research aims to improve face detection accuracy and processing speed using the Faster R-CNN framework.
Applied Faster R-CNN model to face detection.
Trained on the large scale WIDER face dataset.
Evaluated performance on WIDER test set, FDDB, and IJB-A benchmarks.
Achieved state-of-the-art results on the WIDER test set.
Demonstrated improved accuracy on FDDB and IJB-A benchmarks compared to existing methods.

Abstract

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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Cite This Study

Jiang et al. (Mon,) studied this question.

synapsesocial.com/papers/6a16d458b13aec50ea6b8d87 https://doi.org/https://doi.org/10.1109/fg.2017.82