Please use this identifier to cite or link to this item: https://doi.org/10.1109/WACV.2011.5711509
Title: A study on recognizing non-artistic face sketches
Authors: Nejati, H.
Sim, T. 
Issue Date: 2011
Source: Nejati, H.,Sim, T. (2011). A study on recognizing non-artistic face sketches. 2011 IEEE Workshop on Applications of Computer Vision, WACV 2011 : 240-247. ScholarBank@NUS Repository. https://doi.org/10.1109/WACV.2011.5711509
Abstract: Face sketches are being used in eyewitness testimonies for about a century. These sketches are crucial in finding suspects when no photo is available, but a mental image in the eyewitness's mind. However, research shows that current procedures used for eyewitness testimonies have two main problems. First, they can significantly disturb the memories of the eyewitness. Second, in many cases, these procedures result in face images far from their target faces. These two problems are related to the plasticity of the human visual system and the differences between face perception in humans (holistic) and current methods of sketch production (piecemeal). In this paper, we present some insights for more realistic sketch to photo matching. We describe how to retrieve identity specific information from crude sketches, directly drawn by the non-artistic eyewitnesses. The sketches we used merely contain facial component outlines and facial marks (e.g. wrinkles and moles). We compare results of automatically matching two types sketches (trace-over and user-provided, 25 each) to four types of faces (original, locally exaggerated, configurally exaggerated, and globally exaggerated, 249 each), using two methods (PDM distance comparison and PCA classification). Based on our results, we argue that for automatic non-artistic sketch to photo matching, the algorithms should compare the user-provided sketches with globally exaggerated faces, with a soft constraint on facial marks, to achieve the best matching rates. This is because the user-provided sketch from the user's mental image, seems to be caricatured both locally and configurally. © 2010 IEEE.
Source Title: 2011 IEEE Workshop on Applications of Computer Vision, WACV 2011
URI: http://scholarbank.nus.edu.sg/handle/10635/39912
ISBN: 9781424494965
DOI: 10.1109/WACV.2011.5711509
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