Face Liveness Detection

I built a facial liveness detection software designed to verify whether a user is physically present during identity verification. The system tracks real-time facial movements and gesture-based challenges such as nodding, turning right, smiling, and blinking to help prevent spoofing attempts using photos or static images. The solution was developed using Google TensorFlow and computer vision techniques to analyze facial movement patterns during the verification process. In testing, the liveness check system achieved approximately 80% accuracy, demonstrating strong potential for use in authentication, user verification, digital onboarding, KYC processes, and secure access control systems. This project strengthened my experience in artificial intelligence, facial recognition, machine learning, computer vision, TensorFlow integration, biometric authentication, and fraud prevention technology.