Performance Driven Analytical Study for Selecting Optimal Deep Learning Techniques for Masked Face and Iris Biometric Recognition
DOI:
https://doi.org/10.62431/wc2bnf98Keywords:
Masked Face Recognition, Iris Recognition, Deep Learning, Biometrics, Analytical Study, Multimodal AuthenticationAbstract
Masked face recognition poses a significant challenge in modern biometric systems due to occlusion of key facial features. This paper presents a performance-driven analysis of deep learning techniques for face detection, face recognition, and iris recognition. State-of-the-art methods including RetinaFace, MTCNN, YOLO, ArcFace, FaceNet, U-Net-based iris segmentation, and Daugman iris recognition are evaluated on benchmark datasets. A ranking-based framework is proposed to identify optimal techniques for real-world masked scenarios. Results show that RetinaFace excels in detection, ArcFace achieves highest recognition accuracy, and U-Net with Daugman methods provide robust iris segmentation and recognition. The study supports the design of effective multimodal biometric systems.
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Copyright (c) 2026 Akshay Bhardwaj , Rajesh Chauhan , Akshay Tishwan
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