A Django-Enabled Hybrid Framework for Intelligent Face Morph Synthesis and Authentication Resilience
Facial recognition systems are widely used for identity verification but are vulnerable to face morphing attacks, where multiple facial images are blended to form a deceptive identity that can fool recognition models. This project develops a deep learning-based approach to detect such attacks and strengthen biometric authentication systems. It lies in the domain of Artificial Intelligence and Machine Learning, focusing on Computer Vision techniques to differentiate real and morphed facial images for accurate and secure verification. The project involves creating realistic morphed face datasets and building an efficient detection model applicable to border control, ID verification, and digital authentication. Current systems fail against high-quality morphs produced using advanced tools, showing reduced accuracy under variations in lighting, age, and facial accessories. To overcome this, the proposed model combines deep learning-based feature extraction with machine learning classifiers. Morph-2 and Morph-3 datasets are generated using professional morphing tools, and image enhancement with feature fusion is applied to improve accuracy and robustness.