Local Difference of Gaussian Binary Pattern: Robust Features for Face Sketch Recognition

Automatic recognition of face sketches is a challenging problem with application in criminal investigations. We propose a method that allows face sketch recognition across modalities called Local Difference of Gaussian Binary Pattern (LDoGBP). LDoGBP is based on the fact that the sketches are similar to their corresponding photos even though they are prone to shape distoration. This similarity between sketch and photo is captured and used for recognition across modalities. In this method, the face image characteristics are captured in the Difference of Gaussian (DoG) representation of the image patches. The Local Binary Pattern(LBP) corresponding to the DoG representation is then generated. These histograms are concatenated to generate the feature vector corresponding to input image. These feature vectors are compared using Earth Mover´s Distance for recognition. Experiments on the CUFS(Chinese University of Hong Kong (CUHK) Face Sketch Database) and CUFSF (CUHK Face Sketch FERET Database) datesets prove the effectiveness of this feature in Face Sketch Recognition.