Multi-scale Face Detection Based on Single Neural Network

Hongzhe LIU; Shaopeng YANG; Jiazheng YUAN; Xueqiao WANG; Jianming XUE

doi:10.11999/JEIT180163

Volume 40 Issue 11

Oct. 2018

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Article Navigation > Journal of Electronics & Information Technology > 2018 > 40(11): 2598-2605

Hongzhe LIU, Shaopeng YANG, Jiazheng YUAN, Xueqiao WANG, Jianming XUE. Multi-scale Face Detection Based on Single Neural Network[J]. Journal of Electronics & Information Technology, 2018, 40(11): 2598-2605. doi: 10.11999/JEIT180163

Citation:

Hongzhe LIU, Shaopeng YANG, Jiazheng YUAN, Xueqiao WANG, Jianming XUE. Multi-scale Face Detection Based on Single Neural Network[J]. Journal of Electronics & Information Technology, 2018, 40(11): 2598-2605. doi: 10.11999/JEIT180163

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Multi-scale Face Detection Based on Single Neural Network

doi: 10.11999/JEIT180163

1.
Beijing Key Laboratory of Information Service Engineering, Beijing Union University, Beijing 100101, China
2.
Beijing Open University, Beijing 100081, China
3.
Institute of Computer Technology, Beijing Union University, Beijing 100101, China

Funds: The National Natural Science Foundation of China (61571045), The Supporting Plan for Cultivating High Level Teachers in Colleges and Universities in Beijing (IDHT20170511), The National Science and Technology Support Project (2015BAH55F03), The Foundation of Beijing Union University (Zk10201703), The Foundation of Beijing Municipal Education Commission (KM201811417002)

Received Date: 2018-02-07
Rev Recd Date: 2018-07-05

Available Online: 2018-07-23

Publish Date: 2018-11-01

Abstract

Abstract

Face detection is finding and locating all faces in the input image, and then returning the position and size of the faces. It is an important direction of target detection. In order to solve the problem which is caused by the diversity of face size, a new single shot multiscale face algorithm is presented based on feature fusion. This method combines predictions from multiple feature maps with different resolutions to handle faces of various sizes, and the fusion of the feature maps in the shallow layers can improve the detection accuracy of the small size face by introducing the contextual information. Experimental results on the FDDB and WIDERFACE datasets confirm that the proposed method has competitive accuracy. Additionally, the object proposal step is removed, which makes the method fast. The proposed model achieves 87.9%, 93.2% and 93.4% Mean Average Precision (MAP) on the WIDERFACE sub-datasets respectively, at 35 fps. The proposed method outperforms a comparable state-of-the-art HR model, and at the same time improves the speed while ensuring the accuracy.
- Multi-scale face detection,
- Contextual information,
- Feature map fusion,
- Convolution neural network

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References(26)

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