This project implements a deep learning-based system to detect face liveness for applications such as biometric authentication and fraud prevention. It aims to distinguish between real faces and spoofing attempts like photos, videos, or masks.
The solution leverages a Convolutional Neural Network (CNN) model trained on a large dataset of spoof and real face images. Additionally, the project includes a user-friendly interface for browser-based real-time liveness detection.
• Real-Time Detection: Identifies live faces through a webcam interface.
• Spoof Detection: Recognizes spoofing attempts such as photos or videos.
• Browser-Based Application: Built using Flask, HTML, CSS, and JavaScript for a seamless user experience.
• Deep Learning Model: Uses a custom-trained CNN for high accuracy.
Frontend • HTML5 • CSS3 • JavaScript
Backend • Flask (Python)
Deep Learning • Keras/TensorFlow for CNN model development • NumPy, Pandas for data preprocessing • Matplotlib, Seaborn for visualization