Gradient Feature Matching for in-plane rotation invariant face sketch recognition

Automatic recognition of face sketches is a challenging and interesting problem. An artist drawn sketch is compared against a mugshot database to identify criminals. It is a very cumbersome task to manually compare images. This necessitates a pattern recognition system to perform the comparisons. Existing methods fall into two main categories - those that allow recognition across modalities and methods that require a sketch/photo synthesis step and then compare in same modality. The methods that require synthesis require a lot of computing power since it involves high time and space complexity. Our method allows recognition across modalities. It uses the edge features of a face sketch and face photo image to create a feature string called 'edge-string' which is a polar coordinate representation of the edge image. To generate a polar coordinate representation, we need the reference point and reference line. Using the center point of the edge image as the reference point and using a horizontal line as the reference line is the simplest solution. But, it cannot handle in-plane rotations. For this reason, we propose an approach for finding the reference line and the centroid point. The edge-strings of the face photo and face sketch are then compared using the Smith-Waterman algorithm for local string alignments. The face photo that gave the highest similarity score is the photo that matches the test face sketch input. The results on CUHK (Chinese University of Hong Kong) student dataset show the effectiveness of the proposed approach in face sketch recognition.