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Because face sketches represent the original faces in a very concise yet recognizable form, they play an important role in criminal investigations, human visual perception, and face biometrics. In this paper, we compared the performances of humans and a principle component analysis (PCA)-based algorithm in recognizing face sketches. A total of 250 sketches of 50 subjects were involved. All of the sketches were drawn manually by five artists (each artist drew 50 sketches, one for each subject). The experiments were carried out by matching sketches in a probe set to photographs in a gallery set. This study resulted in the following findings: 1) A large interartist variation in terms of sketch recognition rate was observed; 2) fusion of the sketches drawn by different artists significantly improved the recognition accuracy of both humans and the algorithm; 3) human performance seems mildly correlated to that of PCA algorithm; 4) humans performed better in recognizing the caricature-like sketches that show various degrees of geometrical distortion or deviation, given the particular data set used; 5) score level fusion with the sum rule worked well in combining sketches, at least for a small number of artists; and 6) the algorithm was superior with the sketches of less distinctive features, while humans seemed more efficient in handling tonality (or pigmentation) cues of the sketches that were not processed with advanced transformation functions.