Secure User Authentication Using Face Recognition with Liveness Detection

With the increasing reliance on digital platforms and online services, ensuring secure and reliable user authentication has become a critical challenge in modern cybersecurity. Traditional authentication mechanisms such as passwords and PINs are vulnerable to various attacks including phishing, brute-force, replay attacks, and credential theft. To address these limitations, this research proposes a secure user authentication system based on face recognition integrated with liveness detection to prevent spoofing attacks using photos, videos, or masks. The proposed system utilizes deep learning-based facial recognition techniques to identify users based on unique facial features. The authentication pipeline consists of face detection, feature extraction, liveness detection, and identity verification. Face detection is performed using computer vision techniques, while convolutional neural networks (CNN) are employed for accurate facial feature extraction. Liveness detection is incorporated using real-time facial movements such as eye blinking, head movement, and texture analysis to ensure that the input is from a live user rather than a static image. To enhance system security, biometric templates are securely stored using hashing and encryption techniques. The system also maintains structured authentication logs for monitoring and audit purposes. A comparative analysis with traditional password-based authentication demonstrates improved security, usability, and resistance against spoofing and impersonation attacks. Experimental results show that the integration of facial recognition with liveness detection significantly improves authentication accuracy and reduces unauthorized access. The proposed system provides a contactless, user-friendly, and highly secure authentication mechanism suitable for web applications, mobile platforms, and enterprise systems. Future work will focus on multi-factor biometric authentication and improving real-time performance under varying environmental conditions.