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An effective facial spoofing detection approach based on weighted deep ensemble learning

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Abstract

Deep learning has seen successful implementation in various domains, such as natural language processing, image classification, and object detection in recent times. In the field of biometrics, deep learning has also been used to develop effective anti-spoofing systems. Facial spoofing, the act of presenting fake facial information to deceive a biometric system, poses a significant threat to the security of face recognition systems. To address this challenge, we propose, in this paper, an effective and robust facial spoofing detection approach based on weighted deep ensemble learning. Our method combines the strengths of two powerful deep learning architectures, DenseNet201 and MiniVGG. The choice of these two architectures is based on a comparative study between DenseNet201, DenseNet169, VGG16, MiniVGG, and ResNet50, where DenseNet201 and MiniVGG obtained the best recall and precision scores, respectively. Our proposed weighted voting ensemble leverages each architecture-specific capabilities to make the final prediction. We assign weights to each classification model based on its performance, which are determined by a mathematical formulation considering the trade-off between recall and precision. To validate the effectiveness of our proposed approach, we evaluate it on the challenging ROSE-Youtu face liveness detection dataset. Our experimental results demonstrate that our proposed method achieves an impressive accuracy rate of 99% in accurately detecting facial spoofing attacks.

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Availability of data and materials

The research in this project utilized the ROSE-Youtu Face Liveness Detection Dataset, which is a publicly available dataset accessible at the following URL: https://rose1.ntu.edu.sg/dataset/faceLivenessDetection/.

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Funding

This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

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Authors and Affiliations

  1. Computer Science Department, Faculty of Sciences, University Sidi Mohamed Ben Abdallah, Fez, Morocco

    My Abdelouahed Sabri & Assia Ennouni

  2. Physics Department, Faculty of Sciences, University Sidi Mohamed Ben Abdallah, Fez, Morocco

    Abdellah Aarab

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  1. My Abdelouahed Sabri
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  2. Assia Ennouni
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  3. Abdellah Aarab
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Contributions

All authors made substantial contributions to the concept, design, and revision of the paper. AS, AE and AA revise and conceptualized the research idea, formulated the research objectives, and designed the experimental methodology. AS and AE wrote the main manuscript text. AS and AA contributed to the development and implementation.

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Correspondence to My Abdelouahed Sabri.

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Sabri, M.A., Ennouni, A. & Aarab, A. An effective facial spoofing detection approach based on weighted deep ensemble learning. SIViP 18, 935–942 (2024). https://doi.org/10.1007/s11760-023-02818-2

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  • DOI: https://doi.org/10.1007/s11760-023-02818-2

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