Cascaded Static and Dynamic Local Feature Extractions for Face Sketch to Photo Matching

Abstract

The automatic identification of a corresponding photo from a face sketch can assist in criminal investigations. The face sketch is rendered based on the descriptions elicited by the eyewitness. This may cause the face sketch to have some degrees of shape exaggeration that make some parts of the face geometrically misaligned. In this paper, we attempt to address the effect of these influences by a cascaded static and dynamic local feature extraction method so that the constructed feature vectors are built based on the correct patches. In the proposed method, the feature vectors from the local static extraction on a sketch and photo are matched using the nearest neighbors. Then, some n most similar photos are shortlisted based on the nearest neighbors. These photos are eventually re-matched using feature vectors from the local dynamic extraction method. The feature vectors are matched using the L-{1} -distance measure. The experimental results for The Chinese University of Hong Kong (CUHK) Face Sketch Database (CUFS) and CUHK Face Sketch FERET Database (CUFSF) datasets indicate that the proposed method outperforms the state-of-the-art methods.