ITM Web Conf.
Volume 45, 20222021 3rd International Conference on Computer Science Communication and Network Security (CSCNS2021)
|Number of page(s)||5|
|Section||Computer Technology and System Design|
|Published online||19 May 2022|
Face detection of migrating learning based on constrained scene
School of Electronic Engineering, Tianjin University of Technology and Education, Tianjin, China
2 Tianjin Yunzhitong Technology Co., Ltd, Tianjin, China
3 Section 4, Multidisciplinary Digital Publishing Institute, Tianjin, China
4 Tianjin Enterprise Key Laboratory of High-speed Railway Wireless Communication, Tianjin, China
* Corresponding author: email@example.com
Face detection places an important role in face recognition which is a popular choice for biometric systems. To solve the low face detection rate problem of face detection in constrained scenario, an efficient face detection method based on migration learning was proposed in this paper. In the proposed facial detection approach, data-cleaning was firstly used to optimize the face database. Then the Visual Geometry Group 16 (VGG16) deep learning network was improved to realize migration learning by replacing the softmax regression layer with the multi-scale feature detection layer. Finally, the constrained scene face images for testing were detected and labeled by the trained migration learning model. The WIDER FACE dataset was used for experiments. Experiment results showed that the proposed method can successfully perform face detection in the WIDER FACE dataset and obtain more than 90% detection rate.
© The Authors, published by EDP Sciences, 2022
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