Fusion-based Super-Resolution for Facial Identification and Sketch-to-Image Translation

Advancements in technology have inadvertently enabled easier access to crime-related data, resulting in an increase of criminal activities. Law enforcement agencies encounter substantial difficulties in identifying suspects and preventing further crimes. To tackle these challenges, this research introduces a proposed solution of fusion-based super-resolution approach combined with sketch-to-image translation techniques. The method enhances low-quality facial images and using ESRGAN and Real-ESRGAN. A fusion model integrates multiple enhancement strategies, followed by a Siamese model for face verification system. The experimental results demonstrate improved recognition accuracy and image quality, supporting efficient suspect identification. The proposed model shows significant improvement in areas of criminal sketches. This approach effectively strengthens facial recognition systems in real-world surveillance and forensic scenarios.