Analysis Performance Of Face Anti-Spoofing Detection Using Machine Learning
Keywords:
Face Anti-Spoofing Detection, Presentation Attack Detection, Machine Learning, Biometric SecurityAbstract
Face recognition systems are increasingly deployed in security-critical applications such as mobile authentication, banking systems, surveillance, and access control. However, these systems are highly vulnerable to presentation attacks, including printed photos, replayed videos, and 3D mask attacks. Face Anti-Spoofing Detection (FASD) has therefore emerged as an essential security mechanism to distinguish between genuine (live) faces and spoofed attempts. This study presents a performance analysis of Face Anti-Spoofing Detection using machine learning techniques to enhance robustness and reliability. The proposed framework extracts discriminative features related to texture, motion, and reflectance characteristics from facial images and video frames. Machine learning classifiers such as Support Vector Machine (SVM), Random Forest, Decision Tree, and Logistic Regression are implemented and evaluated. The dataset is pre-processed through face detection, normalization, and augmentation to improve model generalization. Performance evaluation is conducted using standard metrics including Accuracy, Precision, Recall and F1-score. Experimental results indicate that ensemble-based classifiers achieve superior performance in detecting spoofing attacks compared to traditional single classifiers. The analysis demonstrates that optimized feature selection and proper handling of class imbalance significantly improve detection accuracy while reducing false acceptance rates. The study highlights the effectiveness of machine learning approaches in mitigating spoofing threats and strengthening biometric authentication systems. Future work may incorporate deep learning architectures and real-time deployment strategies for enhanced security performance.
References
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