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Improving Deep Learning Feature with Facial Texture Feature for Face Recognition

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Abstract

Face recognition in the reality, is a challenging problem, due to varieties in illumination, background, pose etc. Recently, the deep learning based face recognition algorithm is able to learn effective face features to obtain a very impressive performance. However, this kind of face recognition algorithm completely relies on the machine learning based face features, while ignores the useful experience in hand-craft features which have been studied in a long period. Therefore, a face recognition based on facial texture feature aided deep learning feature (FTFA-DLF) is proposed in this paper. The proposed FTFA-DLF is able to combine the benefits of deep learning and hand-craft features. In the proposed FTFA-DLF method, the hand-craft features are texture features extracted from the eyes, nose, and mouth regions. Then, the hand-craft features are used to aid deep learning features by adding both deep learning and hand-craft features into the objective function layer, which adaptively adjusts the deep learning features so that it can better cooperate with the hand-craft features and obtain a better face recognition performance. Experimental results show that the proposed face recognition algorithm on the LFW face database to achieve the accuracy rate of 97.02%.

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China under the Grants 61502364, in part by the Fundamental Research Funds for the Central Universities under the Grant K50510010007, and in part by Weinan normal university Funds 16YKS001.

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Authors and Affiliations

  1. School of Telecommunications Engineering, Xidian University, Xian, 710071, China

    Yunfei Li, Zhaoyang Lu, Jing Li & Yanzi Deng

  2. School of Network Security and Information, Weinan Normal University, Weinan, 714099, China

    Yunfei Li

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  1. Yunfei Li
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  2. Zhaoyang Lu
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  3. Jing Li
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  4. Yanzi Deng
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Correspondence to Yunfei Li.

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Li, Y., Lu, Z., Li, J. et al. Improving Deep Learning Feature with Facial Texture Feature for Face Recognition. Wireless Pers Commun 103, 1195–1206 (2018). https://doi.org/10.1007/s11277-018-5377-2

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