Face Detection with the Faster R-CNN
- 1 May 2017
- conference paper
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 650-657
- https://doi.org/10.1109/fg.2017.82
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.Keywords
This publication has 24 references indexed in Scilit:
- Multi-view Face Detection Using Deep Convolutional Neural NetworksPublished by Association for Computing Machinery (ACM) ,2015
- Unconstrained face detection: State of the art baseline and challengesPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2015
- Fast Edge Detection Using Structured ForestsIEEE Transactions on Pattern Analysis and Machine Intelligence, 2014
- Efficient Boosted Exemplar-Based Face DetectionPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2014
- Detecting and Aligning Faces by Image RetrievalPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2013
- Selective Search for Object RecognitionInternational Journal of Computer Vision, 2013
- Speeded-Up Robust Features (SURF)Computer Vision and Image Understanding, 2008
- Robust Real-Time Face DetectionInternational Journal of Computer Vision, 2004
- Neural network-based face detectionIEEE Transactions on Pattern Analysis and Machine Intelligence, 1998
- Original approach for the localisation of objects in imagesIEE Proceedings - Vision, Image, and Signal Processing, 1994