Abstract
Recently, computer vision based on deep learning is developing rapidly. As an important branch in this area, face recognition has made great progress. The state of art has achieved 99.77% [1] pair-wise verification accuracy on LFW dataset. But the face dataset in the real application environment such as security checking in the station and bank account opening is much more complex than LFW because of face shelter, postures, uneven illumination and the different resolutions and so on. Except that, LFW dataset only contains the faces like western people but little of other area. Since faces from different areas have not consistent distribution, their methods always cannot achieve high recognition accuracy in practice. In this paper, aiming at Asian face, we propose a multiple-step model training method based on CNN network for real scene face recognition in the absence of large amounts of appropriate data. In the whole training process, each step plays an important role. For step1, it mainly enhanced the generalization ability of model by using a large-scale data set from different source. For step2, it improved the specificity of the model by using a smaller dataset which has closer data distribution in the real scene. And for the final step, metric learning is used to make the model more discriminative and expressive. Meanwhile, some strategy including data cleaning, data augmented and data balance are used in our method to improve the whole performance. Experiments show that this method can achieve high-performance for face recognition in the real application scene.
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Acknowledgements
The authors of this paper are members of Shanghai Engineering Research Center of Intelligent Video Surveillance. Our research was sponsored by following projects: the National Natural Science Foundation of China (61403084, 61402116); Program of Science and Technology Commission of Shanghai Municipality (Nos. 15530701300, 15XD15202000); 2012 IoT Program of Ministry of Industry and Information Technology of China; Key Project of the Ministry of Public Security (No. 2014JSYJA007); the Project of the Key Laboratory of Embedded System and Service Computing, Ministry of Education, Tongji University(ESSCKF 2015-03); Shanghai Rising-Star Program (17QB1401000).
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Li, D., Zhang, X., Song, L., Zhao, Y. (2018). Multiple-Step Model Training for Face Recognition. In: Abawajy, J., Choo, KK., Islam, R. (eds) International Conference on Applications and Techniques in Cyber Security and Intelligence. ATCI 2017. Advances in Intelligent Systems and Computing, vol 580. Edizioni della Normale, Cham. https://doi.org/10.1007/978-3-319-67071-3_21
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DOI: https://doi.org/10.1007/978-3-319-67071-3_21
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