Advanced Techniques for Biometric Authentication: Leveraging Deep Learning and Explainable AI

Liveness face detection is essential for modern biometric systems, ensuring that input data is genuine and not derived from a false image or video. Liveness face detection in today's biometric systems will ensure that input comes from a real, live person rather than a manipulated image or video. The novelty of this study lies in combining deep learning models with local interpretable model-agnostic interpretation (LIME) to enhance the interpretability and transparency of facial liveness detection systems. This technology is necessary for preventing spoofing attacks and attempts by hackers to break the security feature via pictures, videos, masks, etc. Spoofing refers to the compromise of a biometric system by providing it with untruthful material, photographs, videos, or masks to gain access. However, if not dealt with, such forms of fraud could affect the security of the biometrics. Liveness detection relies on several strategies, from basic facial actions like blinking and head twists to even more advanced algorithms that can identify natural skin texture and warmth or detect differences at the pixel level between live and static images. Robust liveness detection in biometric authentication significantly enhances security and reliability. The objective of this research is to test the different pre-trained models to detect spoofing attacks and to use LIME to explain the model's predictions. This paper focuses on a dataset of Spoof in Wild with Multiple Attacks Version 2 (SiWMv2), comprising 14 different spoofing techniques, ranging from replay attacks and makeup disguises with paper glasses to more complex ones. Seven pre-trained architectures, VGG16, DenseNet201, InceptionV3, VGG19, ResNet50, MobileNetV2, and Xception, are fine-tuned with the potential for actual automatic liveness identification in facial images. Deep learning approaches achieve superior detection performance against contemporary spoofing techniques. These techniques aim to enhance the interpretability of their predictions. Building on deep learning approaches, LIME is incorporated to improve transparency further. LIME provides visual explanations of the prediction to represent what features support the model's decisions. Our work demonstrates how LIME can effectively give insights that are useful to understand findings on face identification and critically aid in understanding model decisions in security and authentication systems. The key findings of this research show that our method achieves 97.3% accuracy on the SiWMv2 dataset, a significant improvement over baseline models, which average 91.5%. This enhancement shows the effectiveness of our approach in a system that boosts robustness and increases the reliability of liveness detection.