TY - GEN
T1 - Facial sketch synthesis using direct combined model
AU - Tu, Ching Ting
AU - Lien, Jenn Jier James
PY - 2010/11/22
Y1 - 2010/11/22
N2 - Automatically synthesizing the facial sketches of a facial image is highly challenging since facial images typically exhibit a wide range of poses, expressions and scales, and have differing degrees of illumination and/or occlusion. When the facial sketches are to be synthesized in the unique sketching style of a particular artist, the problem becomes even more complex. This study develops an automatic facial sketch synthesis system based on a novel direct combined model (DCM) algorithm carrying three major advantages: First, DCM approach takes account of both the local details of each facial feature and the global geometric structure of the face, and thus the synthesized sketches more accurately mimic the caricatures drawn by the artist. Second, although the training database contains only full-frontal facial images with a neutral expression, sketches with a wide variety of facial poses, gaze directions and facial expressions can be successfully synthesized. Third, previous synthesizing proposals are heavily reliant on the quality of the texture reconstruction results, which in turn are highly sensitive to occlusion and lighting effects in the input image. DCM approach accurately produces lifelike synthesized facial sketches without the need to restore the texture information lost as a result of such unfavorable conditions.
AB - Automatically synthesizing the facial sketches of a facial image is highly challenging since facial images typically exhibit a wide range of poses, expressions and scales, and have differing degrees of illumination and/or occlusion. When the facial sketches are to be synthesized in the unique sketching style of a particular artist, the problem becomes even more complex. This study develops an automatic facial sketch synthesis system based on a novel direct combined model (DCM) algorithm carrying three major advantages: First, DCM approach takes account of both the local details of each facial feature and the global geometric structure of the face, and thus the synthesized sketches more accurately mimic the caricatures drawn by the artist. Second, although the training database contains only full-frontal facial images with a neutral expression, sketches with a wide variety of facial poses, gaze directions and facial expressions can be successfully synthesized. Third, previous synthesizing proposals are heavily reliant on the quality of the texture reconstruction results, which in turn are highly sensitive to occlusion and lighting effects in the input image. DCM approach accurately produces lifelike synthesized facial sketches without the need to restore the texture information lost as a result of such unfavorable conditions.
UR - http://www.scopus.com/inward/record.url?scp=78349282997&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=78349282997&partnerID=8YFLogxK
U2 - 10.1109/ICME.2010.5583537
DO - 10.1109/ICME.2010.5583537
M3 - Conference contribution
AN - SCOPUS:78349282997
SN - 9781424474912
T3 - 2010 IEEE International Conference on Multimedia and Expo, ICME 2010
SP - 1196
EP - 1201
BT - 2010 IEEE International Conference on Multimedia and Expo, ICME 2010
T2 - 2010 IEEE International Conference on Multimedia and Expo, ICME 2010
Y2 - 19 July 2010 through 23 July 2010
ER -