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Face detection and alignment method for driver on highroad based on improved multi-task cascaded convolutional networks

  • Yang Zhang
  • Peihua Lv
  • Xiaobo LuEmail author
  • Jun Li
Article
  • 22 Downloads

Abstract

Driver’s face detection and alignment techniques in Intelligent Transportation System (ITS) under unlimited environment are challenging issues, which are conductive to supervising traffic order and maintaining public safety. This paper proposes the improved Multi-task Cascaded Convolutional Networks (ITS-MTCNN) to realize accurate face region detection and feature alignment of driver’s face on highway, predicting face and feature location via a coarse-to-fine pattern. Moreover, the improved regularization method and effective online hard sample mining technique are proposed in ITS-MTCNN method. Then, the training model and contrast experiment are conducted on our self-build traffic driver’s face database. Finally, the effectiveness of ITS-MTCNN method is validated by comparative experiments and verified under various complex highway conditions. At the same time, average alignment errors on left eye, right eye, nose, left mouth as well as right mouth of the proposed technique are performed. Experimental results show that ITS-MTCNN model shows satisfied performance compared to other state-of-the-art techniques used in driver’s face detection and alignment, keeping robust to the occlusion, varying pose and extreme illumination on highway.

Keywords

Intelligent transportation system Face detection and alignment Multi-task Convolutional networks Deep learning 

Notes

Acknowledgements

We would like to thank the National Natural Science Foundation Projects of China (No.61871123), National Natural Science Foundation of China (No.61374194), National Key Science and Technology Pillar Program of China (No.2014BAG01B03) Key Research and Development Program of Jiangsu Province (No.BE2016739) for funding. In addition, we would like to thank the Public Security Department of Jiangsu Province for providing PSD-HIGHROAD database.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of AutomationSoutheast UniversityNanjingChina
  2. 2.Key Laboratory of Measurement and Control of Complex Systems of Engineering, Ministry of EducationSoutheast UniversityNanjingChina
  3. 3.Faculty of Engineering and Information TechnologyUniversity of Technology SydneyUltimoAustralia

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