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Proceedings Paper

Research on face detection method based on improved MTCNN network
Author(s): Yang Wang; Guowu Yuan D.D.S.; Dong Zheng; Hao Wu; Yuanyuan Pu; Dan Xu
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Paper Abstract

Face detection is one of the important topics in computer vision research and is the basis of many applications. A face detection algorithm based on improved Multi-Task Convolution Neural Network (MTCNN) is proposed in this paper. To increase the accuracy of eye location in complex situations, this method improves the network structure of MTCNN, builds a neural network model based on MTCNN using TensorFlow, and cascades an eye regression network. The Face-Net neural network model was used for training, and the obtained training model was used for detection. Experiments have shown that the accuracy on the LFW dataset is 0.9963 and the accuracy on the YouTube Faces DB dataset is 0.9512.

Paper Details

Date Published: 14 August 2019
PDF: 7 pages
Proc. SPIE 11179, Eleventh International Conference on Digital Image Processing (ICDIP 2019), 111791C (14 August 2019); doi: 10.1117/12.2539617
Show Author Affiliations
Yang Wang, Yunnan Univ. (China)
Guowu Yuan D.D.S., Yunnan Univ. (China)
Dong Zheng, Yunnan Univ. (China)
Hao Wu, Yunnan Univ. (China)
Yuanyuan Pu, Yunnan Univ. (China)
Dan Xu, Yunnan Univ. (China)

Published in SPIE Proceedings Vol. 11179:
Eleventh International Conference on Digital Image Processing (ICDIP 2019)
Jenq-Neng Hwang; Xudong Jiang, Editor(s)

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