Abstract
Biometric recognition, especially facial recognition, has achieved significant success, but it faces challenges like counterfeiting biometric data. This paper proposes a Facial Presentation Attack Detection (PAD) system that incorporates contextual information to identify and discard attacks involving detectable Presentation Attack Instruments (PAIs). The aim is to streamline computational efforts and enhance the subsequent PAD system’s analysis of facial features. The PAD system yields excellent results, achieving a 99% accuracy rate. This high performance is confirmed through the application of a Explainable Artificial Intelligence (XAI) technique, Grad-CAM.
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Acknowledgements
This work was supported in part by Spanish Ministerio de Ciencia e Innovación under Grant PID2021-124176OB-I00, in part by Universidad Rey Juan Carlos, and in part by the Spanish General Directorate of Police. Additionally, the work was guided and supported by all members of the FRAV group: High-performance research group in Facial Recognition and Artificial Vision of Universidad Rey Juan Carlos, who provided the database used for the experiments.
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García-Rubio, I., Gallardo-Cava, R., Ortega-delCampo, D., Guillen-Garcia, J., Palacios-Alonso, D., Conde, C. (2024). Grad-CAM Applied to the Detection of Instruments Used in Facial Presentation Attacks. In: Ferrández Vicente, J.M., Val Calvo, M., Adeli, H. (eds) Artificial Intelligence for Neuroscience and Emotional Systems. IWINAC 2024. Lecture Notes in Computer Science, vol 14674. Springer, Cham. https://doi.org/10.1007/978-3-031-61140-7_27
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