Journal
NEUROCOMPUTING
Volume 299, Issue -, Pages 42-50Publisher
ELSEVIER
DOI: 10.1016/j.neucom.2018.03.030
Keywords
Face detection; Faster RCNN; Convolutional neural networks (CNN); Feature concatenation; Hard negative mining; Multi-scale training
Categories
Ask authors/readers for more resources
In this paper, we present a new face detection scheme using deep learning and achieve the state-of-theart detection performance on the well-known FDDB face detection benchmark evaluation. In particular, we improve the state-of-the-art Faster RCNN framework by combining a number of strategies, including feature concatenation, hard negative mining, multi-scale training, model pre-training, and proper calibration of key parameters. As a consequence, the proposed scheme obtained the state-of-the-art face detection performance and was ranked as one of the best models in terms of ROC curves of the published methods on the FDDB benchmark.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available