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Multi-pose face reconstruction and Gabor-based dictionary learning for face recognition

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Abstract

Many methods based on data and model analysis have been proposed to overcome the adverse effect of poses, occlusion, and illumination on face recognition. Frontal face generation from multi-pose faces is still a widely studied and challenging problem due to its ill-posed. We focus on using a simple model to overcome the effect of pose changes on face recognition. This paper proposes a multi-pose face reconstruction model (MPFR) to generate available face information and combines the model with Gabor-based dictionary learning methods to learn discriminant features. The MPFR is adopted to generate the frontal face image. Given the distortion of the image only generated by Generative Adversarial Networks, the identity loss functions and the symmetry loss function are utilized in the multi-pose faces reconstruction model to reconstruct a more realistic reconstruction frontal image. Besides, we combine discriminative dictionary learning with Gabor features to better express face features for image classification. We report qualitative visualization results and quantitative recognition results of the MPFR model. Further, the experimental results demonstrate the application of the MPFR model in face recognition.

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Acknowledgements

This study is supported by Open Project of Key Laboratory of Ministry of Public Security for Road Traffic Safety(No.2021ZDSYSKFKT04), Jiangsu Engineering Research Center of Digital Twinning Technology for Key Equipment in Petrochemical Process(No.DT2020720).

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Authors and Affiliations

  1. School of Computer Science and Artificial Intelligence, Changzhou University, Changzhou, 213164, China

    Huanjie He, Jiuzhen Liang & Zhenjie Hou

  2. School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, 214122, China

    Lan Di

  3. Department of Mathematics and Statistics, University of North Carolina Charlotte, Charlotte, USA

    Yunfei Xia

  4. Jiangsu Engineering Research Center of Digital Twinning Technology for Key Equipment in Petrochemical Process, Changzhou University, Changzhou, 213164, China

    Jiuzhen Liang

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  1. Huanjie He
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  2. Jiuzhen Liang
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  4. Lan Di
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  5. Yunfei Xia
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Correspondence to Jiuzhen Liang.

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He, H., Liang, J., Hou, Z. et al. Multi-pose face reconstruction and Gabor-based dictionary learning for face recognition. Appl Intell 53, 16648–16662 (2023). https://doi.org/10.1007/s10489-022-04336-z

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