Abstract:
One of the important cues 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
the 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 algorithms and
two commercial face recognition systems.
