Enhancing Face Authentication:CNN-Based Anyi-Spoofing and Liveness Detection with Image Processing
Keywords:
Face Authentication, Anti-Spoofing, Liveness Detection, Convolutional Neural Network (CNN), Image Processing, Biometric Security, Deep Learning, Facial Recognition.Abstract
Face authentication systems are increasingly deployed in applications such as mobile security, banking, and access control; however, they remain vulnerable to spoofing attacks using photographs, videos, and 3D masks. This paper proposes a robust face authentication framework based on Convolutional Neural Networks (CNN) integrated with image processing techniques for effective anti-spoofing and liveness detection. The system employs preprocessing methods including illumination normalization, noise reduction, and texture enhancement to improve input image quality. A deep CNN model is utilized to extract discriminative spatial features and identify spoofing artifacts such as texture distortions and presentation inconsistencies. Furthermore, liveness detection is enhanced through dynamic analysis techniques such as eye-blink detection, facial motion tracking, and reflection-based cues. The proposed model is trained and evaluated on diverse datasets to ensure generalization across varying environmental conditions. Experimental results indicate improved accuracy, reduced false acceptance rate (FAR), and enhanced robustness compared to conventional methods. The framework demonstrates strong potential for real-time, secure biometric authentication in practical applications.
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