CSDL Home IEEE Transactions on Pattern Analysis & Machine Intelligence 2009 vol.31 Issue No.11 - November
Issue No.11 - November (2009 vol.31)
Xiaogang Wang , The Chinese University of Hong Kong, Hong Kong
Xiaoou Tang , The Chinese University of Hong Kong, Hong Kong
In this paper, we propose a novel face photo-sketch synthesis and recognition method using a multiscale Markov Random Fields (MRF) model. Our system has three components: 1) given a face photo, synthesizing a sketch drawing; 2) given a face sketch drawing, synthesizing a photo; and 3) searching for face photos in the database based on a query sketch drawn by an artist. It has useful applications for both digital entertainment and law enforcement. We assume that faces to be studied are in a frontal pose, with normal lighting and neutral expression, and have no occlusions. To synthesize sketch/photo images, the face region is divided into overlapping patches for learning. The size of the patches decides the scale of local face structures to be learned. From a training set which contains photo-sketch pairs, the joint photo-sketch model is learned at multiple scales using a multiscale MRF model. By transforming a face photo to a sketch (or transforming a sketch to a photo), the difference between photos and sketches is significantly reduced, thus allowing effective matching between the two in face sketch recognition. After the photo-sketch transformation, in principle, most of the proposed face photo recognition approaches can be applied to face sketch recognition in a straightforward way. Extensive experiments are conducted on a face sketch database including 606 faces, which can be downloaded from our Web site (http://mmlab.ie.cuhk.edu.hk/facesketch.html).
Face recognition, face sketch synthesis, face sketch recognition, multiscale Markov random field.
Xiaogang Wang, Xiaoou Tang, "Face Photo-Sketch Synthesis and Recognition", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.31, no. 11, pp. 1955-1967, November 2009, doi:10.1109/TPAMI.2008.222