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Dual sparse learning via data augmentation for robust facial image classification

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Abstract

Data augmentation has been utilized to improve the accuracy and robustness of face recognition algorithms. However, most of the previous studies focused on using the augmentation techniques to enlarge the feature set, while the diversity produced by the virtual samples lacked sufficient attention. In sparse dictionary learning-based face recognition, \(l_1\)-based sparse representation (SR) and SVD-based dictionary learning (DL) both have shown promising performance. How to utilize both of them in an enhanced training process by data augmentation is still unclear. This paper proposes a novel method that utilizes the sample diversity generated by data augmentation and integrates sparse representation with dictionary learning, to learn dual sparse features for robust face recognition. An additional feature set is created by applying sample augmentation via simply horizontal flipping of face images. The two sparse models, \(l_1\)-based SR and SVD-based DL, are integrated together using our new proposed objective function. Under two-level fusion of both data and classifiers, the diversity between two training sets is well learned and utilized, in three implementations, to obtain a robust face recognition. After conducting extensive experiments on some popular facial datasets, we demonstrate the proposed method can produce a higher classification accuracy than many state-of-the-art algorithms, and it can be considered as a promising option for image-based face recognition. Our code is released at GitHub.

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Notes

  1.  The code to extract deep features using FaceNet via TensorFlow - https://github.com/zengsn/facenet.

  2.  The code to extract deep features using ResNet via TensorFlow - https://github.com/zengsn/TF_FeatureExtraction.

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Acknowledgements

This research was funded by the University of Macau (no. MYRG2018-00053-FST) and National Natural Science Foundation of China (NSFC) (no. 61976107), Indigenous Innovation’s Capability Development Program of Huizhou University (no. hzu201907), and Science and Technology Program of Huizhou City (Grant no. 2016X0422037). This work was performed in part at the High Performance Computing Cluster (HPCC) which is supported by Information and Communication Technology Office (ICTO) of the University of Macau. We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan Xp GPU used for this research.

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Authors and Affiliations

  1. Pattern Analysis and Machine Intelligence Group, Department of Computer and Information Science, University of Macau, Macau, China

    Shaoning Zeng & Bob Zhang

  2. School of Computer Science and Engineering, Huizhou University, Huizhou, Guangdong, China

    Shaoning Zeng

  3. Electronics and Computer Science, University of Southampton, Southampton, SO17 1BJ, UK

    Yanghao Zhang

  4. College of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang, Jiangsu, China

    Jianping Gou

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  1. Shaoning Zeng
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Correspondence to Bob Zhang.

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Zeng, S., Zhang, B., Zhang, Y. et al. Dual sparse learning via data augmentation for robust facial image classification. Int. J. Mach. Learn. & Cyber. 11, 1717–1734 (2020). https://doi.org/10.1007/s13042-020-01067-w

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