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Fine Tuning Dual Streams Deep Network with Multi-scale Pyramid Decision for Heterogeneous Face Recognition

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Abstract

In this paper, we propose a novel method called fine tuning dual streams deep network (FTDSDN) with multi-scale pyramid decision (MsPD) for solving heterogeneous face recognition task. As an extension of classical CNNs, FTDSDN can remove highly non-linear modality information and reserve the discriminative information using Rayleigh quotient objective function. Furthermore, we develop a powerful joint decision strategy called MsPD to adaptively adjust the weight of sub structure and obtain more robust classification performance. Experimental results show our proposed method achieves better performance on the challenging CASIA NIR-VIS 2.0 database, the heterogeneous face biometrics database, the CUHK face sketch FERET database, and the CUHK face sketch database, which demonstrates the effectiveness of our proposed approach.

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China under Grants 61673402, 61273270, and 60802069, in part by the Natural Science Foundation of Guangdong under Grants 2017A030311029, 2016B010109002, 2015B090912001, 2016B010123005, and 2017B090909005, in part by the Science and Technology Program of Guangzhou under Grants 201704020180 and 201604020024, and in part by the Fundamental Research Funds for the Central Universities of China.

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  1. School of Electronics and Information Engineering, Sun Yat-sen University, Guangzhou, Guangdong, China

    Weipeng Hu & Haifeng Hu

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  1. Weipeng Hu
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  2. Haifeng Hu
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Correspondence to Haifeng Hu.

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Hu, W., Hu, H. Fine Tuning Dual Streams Deep Network with Multi-scale Pyramid Decision for Heterogeneous Face Recognition. Neural Process Lett 50, 1465–1483 (2019). https://doi.org/10.1007/s11063-018-9942-1

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