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Face recognition based on multi-scale local directional value

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Abstract

This paper presents a simple, yet robust scheme to improve the LDP-based methods in face recognition. Firstly, a generalized operator, called local directional value (LDV), is introduced to reduce the influence of local gray variations on the traditional LDP-based operators. Then, the multi-scale scheme is presented, which extends the local structure of the traditional LDP-based descriptors from 3 × 3 neighborhoods to multi-scale circular neighborhoods. The new scheme is also robust to noise and the gray variations. The proposed scheme is evaluated with the state-of-the-art methods on popular benchmark face databases and the experimental results show that the given method performs better than the traditional techniques.

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Acknowledgements

The authors would like to thank the groups for sharing their face databases, Caltech, Orl, Georgia and Face94 databases.

This work was supported by the National Science Foundation of China (NSFC) (61572173), the project of science and technology of Henan province (172102210272), Henan Science and Technology Innovation Outstanding Youth Program (184100510009).

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  1. School of Computer Science and Technology, Henan Polytechnic University, Shiji Street, Jiaozuo, 454000, China

    Xiaosheng Wu & Junding Sun

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  1. Xiaosheng Wu
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  2. Junding Sun
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Correspondence to Junding Sun.

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Wu, X., Sun, J. Face recognition based on multi-scale local directional value. Multimed Tools Appl 79, 2409–2425 (2020). https://doi.org/10.1007/s11042-019-08245-1

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