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Image Enhancement Effects on the Forensic Facial Recognition System

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Advances in Intelligent Automation and Soft Computing (IASC 2021)

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 80))

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Abstract

In the field of forensic identification of human images, face recognition technology has become an important quantitative examination method for face similarity examination. Nevertheless, face image quality has its effects on recognition performance of face recognition systems. Image enhancement technology can improve image visual presentation quality, but whether it has effects on the state-of-the-art deep-learning based face recognition systems, especially in the application of the practical case scenarios of forensic identification of human images, is still unknown. In this paper, we studied the statistical effects of the four common used single-image super-resolution enhancement techniques on the recognition performance of the face recognition system by using the face image materials collected from 33 real cases of forensic human image identification. The results showed that not all image enhancement techniques had positive effects. Among them, the self-snake model based method could improve recognition performance, while the other methods had negative or none significant effects on the face recognition system.

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Acknowledgments

This work was supported by Ministry of Finance, PR China (No. GY2021G-3 and GY2020G-8).

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Authors and Affiliations

  1. Academy of Forensic Science, Shanghai, China

    Jinhua Zeng, Qimeng Lu & Shaopei Shi

  2. East China University of Political Science and Law, Shanghai, China

    Xiulian Qiu

  3. Department of Electronics and Information Engineering, Tongji Zhejiang College, Zhejiang, China

    Huaping Zhu

Authors
  1. Jinhua Zeng
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  2. Xiulian Qiu
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  3. Qimeng Lu
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  4. Huaping Zhu
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  5. Shaopei Shi
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Corresponding author

Correspondence to Shaopei Shi .

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Editors and Affiliations

  1. Electronics and Computer Engineering Technology, Indiana State University, Terre Haute, IN, USA

    Xiaolong Li

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Zeng, J., Qiu, X., Lu, Q., Zhu, H., Shi, S. (2022). Image Enhancement Effects on the Forensic Facial Recognition System. In: Li, X. (eds) Advances in Intelligent Automation and Soft Computing. IASC 2021. Lecture Notes on Data Engineering and Communications Technologies, vol 80. Springer, Cham. https://doi.org/10.1007/978-3-030-81007-8_89

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