Face Liveness Detection System For Preventing Spoofing Attacks

Face recognition systems are increasingly used for authentication in various applications such as banking, mobile security, and access control. However, these systems are highly vulnerable to spoofing attacks, including the use of printed photographs, video replays, and masks. To address this issue, this paper proposes a Face Liveness Detection System that can effectively distinguish between real human faces and fake rep- resentations. The proposed system utilizes computer vision and machine learning techniques to analyze dynamic facial features such as eye blinking, facial movements, and texture patterns. A dataset containing both real and spoofed facial inputs is used to train and evaluate the model. The system operates in real-time using a webcam and provides accurate classification of live and fake inputs. Experimental results demonstrate improved detection accuracy and robustness against common spoofing methods. This approach enhances the reliability and security of face recognition systems, making it suitable for real-world biometric authentication applications.