Please use this identifier to cite or link to this item: https://doi.org/10.1007/978-3-642-40246-3_4
Title: Eyewitness face sketch recognition based on two-step bias modeling
Authors: Nejati, H.
Zhang, L.
Sim, T. 
Keywords: Biologically Inspired
Biometrics
Face Sketch Recognition
Image Processing
Issue Date: 2013
Source: Nejati, H.,Zhang, L.,Sim, T. (2013). Eyewitness face sketch recognition based on two-step bias modeling. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 8048 LNCS (PART 2) : 26-33. ScholarBank@NUS Repository. https://doi.org/10.1007/978-3-642-40246-3_4
Abstract: Over 30 years of psychological studies on eyewitness testimonies procedures show severe flaws including ignoring human face perception biases that render these procedures unreliable. In addition, recent studies show that current automatic face sketch recognition methods are only tested on over simplified databases, and therefore cannot address the real cases. We here present a face sketch recognition method based on non-artistic sketches in which we firstly estimate and remove personal face perception biases from face sketches, and then recognize them based on a psychologically inspired matching technique. In addition, we use a general-specific modeling that only needs a few training samples for each individual for an accurate and robust performance. In our experiments, we tested accuracy and robustness against previous works, and the effect of number of training samples on the accuracy of our method. © 2013 Springer-Verlag.
Source Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
URI: http://scholarbank.nus.edu.sg/handle/10635/78142
ISBN: 9783642402456
ISSN: 03029743
DOI: 10.1007/978-3-642-40246-3_4
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