Face Liveness Detection is a key technology in facial recognition systems that ensures the image captured by a webcam is from a live person, rather than a static or fake representation. This feature is critical for improving the security, reliability, and automation of face recognition systems, which are vulnerable to increasing security threats such as identity fraud.
Our project, liveliness detection system, is designed to strengthen the existing facial recognition systems by addressing security challenges and reducing the risk of unauthorized access. This system enhances the overall reliability of authentication platforms.
- Spoof Detection: Capable of detecting fake images to prevent unauthorized access.
- Liveness Analysis: Analyzes images to recognize live interactions in real-time.
- Easy Integration: Can be easily incorporated into existing security and authentication systems.
- OpenCV: Used for image preprocessing tasks such as face detection, scaling, and augmentation.
- TensorFlow/Keras: Core machine learning frameworks for building and training models, including Convolutional Neural Networks (CNNs) to detect spoofing.
- Pandas & Numpy: Utilized for efficient data handling and numeric operations during dataset processing.
This technology is ideal for enhancing security in:
- Facial Recognition Systems
- Mobile Device Authentication
- Access Control Systems
- Fraud Prevention Mechanisms