Face Recognition with Anti-Spoofing: A Deep Learning-Based Approach for Secure Biometric Authentication

Face Recognition is a critical biometric authentication technology that depends on contactless and easy identification using readily available digital cameras. Face recognition can be used in Smartphones, access control systems, attendance in companies, Banking, Healthcare, Examinations, Surveillance etc. However, face recognition system implemented only for recognition purposes is vulnerable to attacks. These attacks are called presentation attacks. This included print, photo, replay,3-D mask and digital attacks. A novel deep learning-based approach for face recognition and face anti-spoofing (presentation attack detection) is proposed here. It is argued that designing the system in a way that 2-Step authentication is performed is optimal. The model includes face detection, preprocessing, anti-spoofing classification, feature extraction, face embeddings comparisons and authentication. Texture, motion, image quality, depth, illumination, and temporal features can be used for presentation attack detection. The Research will leverage convolutional neural networks to detect spoofing attacks. Existing open- sourced face anti-spoofing databases will be used to design robust models. Accuracy, precision, recall, F1-score, FAR, FRR, and EER will be calculated to evaluate the performance. In addition, paper will review security, privacy, database dependency, domain generalization, computational costs and emerging presentation attacks. The proposal framework will serve as a foundation for future research on Multimodal biometrics, Vision transformers, edge AI and Unknown attack detection using the listed databases.