Digital Face Recognition Techniques for Matching with Sketches

Abstract

Face recognition is a well studied problem in many application domains. However, matching sketches with digital face images is a very important law enforcement application that has received relatively less attention. The state-of art face recognition algorithms cannot be used directly and require additional processing to address the non-linear variations present in sketches and digital face images. One of the important clues in solving crimes and apprehending criminals is matching sketches with digital face images. This paper presents an automated algorithm that extracts discriminating information from local regions of both sketches and digital face images. Structural information along with minute details present in local facial regions are encoded using multi scale circular Weber’s local descriptor. Further, an evolutionary Memetic Optimization is proposed to assign optimal weights to every local facial region to boost the identification performance. Since, forensic sketches or digital face images can be of poor quality, a pre-processing technique is used to enhance the quality of images and improve the identification performance. Comprehensive experimental evaluation on different sketch databases show that the proposed algorithm yields better identification performance compared to existing face recognition algorithms and two commercial face recognition systems.