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A new face presentation attack detection method based on face-weighted multi-color multi-level texture features

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Abstract

Biometric data (facial, voice, fingerprint, and retinal scans, for example) are widely used in identification due to their unique and irreversible nature. Facial recognition technologies are employed in a wide range of applications due to their contactless nature and convenience. However, technological advancements and the availability of access to personal information have rendered these biometric systems susceptible to attacks utilizing fake faces. As a result, the issue of anti-spoofing has emerged as a critical one in the field of facial recognition. This study proposes a joint face presentation attack (FPA) detection method based on face-weighted multi-color multi-level LBP features extracted from the combination of device-dependent HSV and device-independent L*a*b* color spaces. The facial images were converted to HSV and L*a*b* color spaces. Three levels of regional LBP features were extracted from each color channel and then concatenated. Finally, a Multi-Color Multi-Level LBP (MCML_LBP) feature vector was obtained. In addition, the Face Weighted MCML_LBP feature vector was produced (FW_MCML_LBP) by adding the LBP histogram extracted from the central region of the normalized image. The feature vectors are used to train an SVM classifier after reducing their size using PCA. Twenty-five different test scenarios were subjected to experimentation on the CASIA and Replay-Attack databases. 2.11% EER and 0.19% HTER were achieved on CASIA (Overall) and Replay-Attack (Grandtest) databases, respectively, using the L*a*b color space and the proposed feature extraction method. The results of the study showed that the proposed method was successful in FPA detection compared to the state-of-the-art methods.

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Acknowledgements

The authors would like to thank the journal reviewers for their valuable suggestions.

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Authors and Affiliations

  1. Bayburt University, Bayburt, Turkey

    Uğur Turhal

  2. Karadeniz Technical University, Trabzon, Turkey

    Asuman Günay Yılmaz & Vasif Nabiyev

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  1. Uğur Turhal
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  2. Asuman Günay Yılmaz
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Correspondence to Uğur Turhal.

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The authors have no relevant financial or nonfinancial interests to disclose. The authors have no conflicts of interest to declare relevant to the content of this article. All authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest or nonfinancial interest in the subject matter or materials discussed in this manuscript. The authors have no financial or proprietary interests in any material discussed in this article.

Data availability

The databases that support the findings of this study are available from The Center for Biometrics and Security Research (CASIA-FASD) and Idiap Research Institute (Replay-Attack). However, restrictions apply to the availability of these databases, which were used under license in this study and are not publicly available. Databases are, however, available from the authors upon reasonable request and with permission of [28, 29].

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Turhal, U., Günay Yılmaz, A. & Nabiyev, V. A new face presentation attack detection method based on face-weighted multi-color multi-level texture features. Vis Comput 40, 1537–1552 (2024). https://doi.org/10.1007/s00371-023-02866-2

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