An unsupervised method for face photo-sketch synthesis and recognition

Abstract

Face recognition is considered one of the most essential applications of Biometrics for personal identification. Face sketch recognition is a special case of face recognition, and it is very important for forensic applications. In this paper, we propose an unsupervised method for face photo-sketch recognition by synthesizing a pseudo-sketch from a single photo. The proposed method is the first unsupervised method that deals with face sketch recognition. The proposed photo-sketch synthesis step consists of two main steps, namely: edge detection and hair detection, which are applied on the grayscale image of the photo image. In the recognition step, the artist sketch is compared with the generated pseudo-sketch. PCA and LDA are used to extract features from the sketch images. The k-nearest neighbor classifier with Euclidean distance is used in the classification step. We use the CUHK database to test the performance of the proposed Method. Results for the synthesized sketches are compared with state-of-the-art methods, e.g., Local Linear Embedding (LLE) and Eigen transformation. The experimental results show that the proposed method generates a clear synthesis sketch and it defines persons more accurate than other methods. Moreover, in the recognition step, the proposed method achieves a recognition rate at the 1-nearest neighbor (rank1: first-match) range from 82% with PCA to 94% with LDA. The highest recognition rate is obtained at the 5-nearest neighbor (rank 5) is 98% that is better than some of the state-of-the-art methods.