A Novel Decision Support for Composite Sketch Matching using Fusion of Probabilistic Neural Network and Dictionary Matching

Traditional pencil sketches drawn by skilled pencil sketch artists are a product of illustration based on exaggeration; there is always some amount of discrepancy between the description of eye-witnesses and depiction of the offender by the sketch artist. To overcome this difficulty, law enforcement agencies worldwide have started using composite sketches (sketches created using computer). Composite sketches have obviated the need of a skilled sketch artist. Composite sketches can be easily drawn by eyewitnesses using face design system software (SketchCop FACETTE) in a very short time period without any prior specialized software training. Matching composite sketches with photos available in database are still a challenging task. In this paper, a novel technique is proposed to match composite sketches with photos available in database. The key contribution of the proposed system in this paper is an efficient and novel methodology developed to match composite sketches with photos available in databases which could be used by law enforcement agencies. The photos taken from law enforcement agencies databases are passed to face detection module. On the detected faces and composite sketches, feature extraction and classification are performed using Multi-resolution uniform Local Binary Pattern (LBP) and Probabilistic Neural Network (PNN) to generate score 1. Similarly, on detected faces and composite sketches, feature extraction and classification are again performed using Dictionary Matching (DM) to generate score 2. The generated scores are collected using Dempster-Shafer (DS) theory and Proportional Conflict Redistribution rule no. 5 (PCR5). In this study, the authors have performed pilot testing of their technique and results of their analysis are presented to the readers.