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 the sketch drawing; (2) given a face sketch drawing, synthesizing a photo; (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 norm 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 learnt. From a training set which contains photo-sketch pairs, the joint photo-sketch model is learnt at multiple scales using a multiscale MRF model. By transforming a face photo into 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.