IEEE Transactions on Pattern Analysis and Machine Intelligence
The FERET Evaluation Methodology for Face-Recognition Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Empirical Evaluation Techniques in Computer Vision
Empirical Evaluation Techniques in Computer Vision
Sum Versus Vote Fusion in Multiple Classifier Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Framework for Recognizing a Facial Image from a Police Sketch
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
Comparisons between Human and Computer Recognition of Faces
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
Human Recognition of Familiar and Unfamiliar People in Naturalistic Video
AMFG '03 Proceedings of the IEEE International Workshop on Analysis and Modeling of Faces and Gestures
Face Sketch Synthesis and Recognition
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Face recognition: A literature survey
ACM Computing Surveys (CSUR)
EvoFIT: A holistic, evolutionary facial imaging technique for creating composites
ACM Transactions on Applied Perception (TAP)
Handbook of Face Recognition
Handbook of Multibiometrics (International Series on Biometrics)
Handbook of Multibiometrics (International Series on Biometrics)
Reliable Face Recognition Methods: System Design, Implementation and Evaluation (International Series on Biometrics)
Multi-frame Approaches To Improve Face Recognition
WMVC '07 Proceedings of the IEEE Workshop on Motion and Video Computing
Journal of Cognitive Neuroscience
Face Photo-Sketch Synthesis and Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
A survey of approaches and challenges in 3D and multi-modal 3D+2D face recognition
Computer Vision and Image Understanding
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans - Special section: Best papers from the 2007 biometrics: Theory, applications, and systems (BTAS 07) conference
Comparing Human and Automatic Face Recognition Performance
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Fusing Face-Verification Algorithms and Humans
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Human Face Image Searching System Using Sketches
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Face Recognition Algorithms Surpass Humans Matching Faces Over Changes in Illumination
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Circuits and Systems for Video Technology
Discussion: Reverse caricatures effects on three-dimensional facial reconstructions
Image and Vision Computing
Style and abstraction in portrait sketching
ACM Transactions on Graphics (TOG) - SIGGRAPH 2013 Conference Proceedings
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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.