Matching Forensic Sketches to Mug Shot Photos using Speeded Up Robust Features

Abstract

The problem that is dealt in the project is to match a forensic sketch against a gallery of mug shot photos. Research in past decade offered solutions for matching sketches that were drawn while looking at the subject (viewed sketches). In this thesis, emphasis is made on matching the forensic sketches, which are the sketches drawn by specially trained artists in police department based on the description of subject by an eyewitness. Recently, a method for forensic sketch matching using LFDA (Local Feature based Discriminant Analysis) was published. Here, the same problem is addressed using a novel preprocessing technique combined with a local feature descriptor called SURF (Speeded Up Robust Features). In our method, first, the images are preprocessed using a novel preprocessing technique suitable for forensic sketch matching that is based on the cognitive research on human memory. After the preprocessing, SURF is used for matching. SURF extracts features in the form of 64-variable vectors for each image. Then all these vectors of one image are combined to form the SURF descriptor vector for that image. These descriptor vectors are then used for matching. This method of forensic sketch matching was applied to match a dataset of 64 Forensic Sketches against a gallery of 1058 photos. From our experiments, it was observed that our approach of image preprocessing combined with SURF had shown promising results with a good accuracy.