Publication Type
Journal Article
Version
publishedVersion
Publication Date
7-2018
Abstract
In this paper, we present a new face detection scheme using deep learning and achieve the state-of-the-art 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
Keywords
Face detection, Faster RCNN, Convolutional neural networks (CNN), Feature concatenation, Hard negative miningMulti-scale training
Discipline
Databases and Information Systems
Research Areas
Data Science and Engineering
Publication
Neurocomputing
Volume
299
First Page
42
Last Page
50
ISSN
0925-2312
Identifier
10.1016/j.neucom.2018.03.030
Publisher
Elsevier
Citation
SUN, Xudong; WU, Pengcheng; and HOI, Steven C. H..
Face detection using deep learning: An improved faster RCNN approach. (2018). Neurocomputing. 299, 42-50.
Available at: https://ink.library.smu.edu.sg/sis_research/3998
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1016/j.neucom.2018.03.030