This project introduces a simple and efficient method with the growing adoption of facial recognition systems in authentication and access control, ensuring the liveness of the detected face has become crucial to prevent spoofing attacks using photos, videos, or 3D masks. This project proposes an enhanced multi-feature face liveness detection system that integrates a range of biometric and behavioural features including eye blink detection, Forehead muscle movement and Pupil movement tracking based on the fixed point. The proposed system is built using a modular technology stack comprising Python, OpenCV, Media Pipe, Dlib, EAR (eye aspect ratio), gaze direction estimation with real-time processing capabilities that make it suitable for deployment in security-critical environments. Experimental evaluation shows that combining multiple liveness cues-especially eye blinking and forehead dynamics significantly improves detection accuracy and resilience against high-quality spoofing attacks. This multi-modal, real-time and user-interactive approach contributes to the advancement of biometric security by offering a more human-like, behaviour-aware detection mechanism.