The purpose of this paper is to present the novel technique to solve the recognition errors in invariant range image multi-pose face recognition. The scale, center and pose error problems were solved by using the geometric transform. Range image face data (RIFD) was obtained from a laser range finder and was used in the model to generate multi-poses. Each pose data size was reduced by linear reduction. The reduced RIFD was transformed to the gradient face model for facial feature image extraction and also for matching using the Membership Matching Score model. Using this method, the results from the experiment are acceptable although the size of gradient face image data is quite small (659 elements). Three-Layer Matching Search was the algorithm designed to reduce the access timing to the most accurate and similar pose position. The proposed algorithm was tested using facial range images from 130 people with normal facial expressions and without eyeglasses. The results achieved the mean success rate of 95.67 percent of
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Seri PANSANG, Boonwat ATTACHOO, Chom KIMPAN, Makoto SATO, "Invariant Range Image Multi-Pose Face Recognition Using Gradient Face, Membership Matching Score and 3-Layer Matching Search" in IEICE TRANSACTIONS on Information,
vol. E88-D, no. 2, pp. 268-277, February 2005, doi: 10.1093/ietisy/e88-d.2.268.
Abstract: The purpose of this paper is to present the novel technique to solve the recognition errors in invariant range image multi-pose face recognition. The scale, center and pose error problems were solved by using the geometric transform. Range image face data (RIFD) was obtained from a laser range finder and was used in the model to generate multi-poses. Each pose data size was reduced by linear reduction. The reduced RIFD was transformed to the gradient face model for facial feature image extraction and also for matching using the Membership Matching Score model. Using this method, the results from the experiment are acceptable although the size of gradient face image data is quite small (659 elements). Three-Layer Matching Search was the algorithm designed to reduce the access timing to the most accurate and similar pose position. The proposed algorithm was tested using facial range images from 130 people with normal facial expressions and without eyeglasses. The results achieved the mean success rate of 95.67 percent of
URL: https://global.ieice.org/en_transactions/information/10.1093/ietisy/e88-d.2.268/_p
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@ARTICLE{e88-d_2_268,
author={Seri PANSANG, Boonwat ATTACHOO, Chom KIMPAN, Makoto SATO, },
journal={IEICE TRANSACTIONS on Information},
title={Invariant Range Image Multi-Pose Face Recognition Using Gradient Face, Membership Matching Score and 3-Layer Matching Search},
year={2005},
volume={E88-D},
number={2},
pages={268-277},
abstract={The purpose of this paper is to present the novel technique to solve the recognition errors in invariant range image multi-pose face recognition. The scale, center and pose error problems were solved by using the geometric transform. Range image face data (RIFD) was obtained from a laser range finder and was used in the model to generate multi-poses. Each pose data size was reduced by linear reduction. The reduced RIFD was transformed to the gradient face model for facial feature image extraction and also for matching using the Membership Matching Score model. Using this method, the results from the experiment are acceptable although the size of gradient face image data is quite small (659 elements). Three-Layer Matching Search was the algorithm designed to reduce the access timing to the most accurate and similar pose position. The proposed algorithm was tested using facial range images from 130 people with normal facial expressions and without eyeglasses. The results achieved the mean success rate of 95.67 percent of
keywords={},
doi={10.1093/ietisy/e88-d.2.268},
ISSN={},
month={February},}
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TY - JOUR
TI - Invariant Range Image Multi-Pose Face Recognition Using Gradient Face, Membership Matching Score and 3-Layer Matching Search
T2 - IEICE TRANSACTIONS on Information
SP - 268
EP - 277
AU - Seri PANSANG
AU - Boonwat ATTACHOO
AU - Chom KIMPAN
AU - Makoto SATO
PY - 2005
DO - 10.1093/ietisy/e88-d.2.268
JO - IEICE TRANSACTIONS on Information
SN -
VL - E88-D
IS - 2
JA - IEICE TRANSACTIONS on Information
Y1 - February 2005
AB - The purpose of this paper is to present the novel technique to solve the recognition errors in invariant range image multi-pose face recognition. The scale, center and pose error problems were solved by using the geometric transform. Range image face data (RIFD) was obtained from a laser range finder and was used in the model to generate multi-poses. Each pose data size was reduced by linear reduction. The reduced RIFD was transformed to the gradient face model for facial feature image extraction and also for matching using the Membership Matching Score model. Using this method, the results from the experiment are acceptable although the size of gradient face image data is quite small (659 elements). Three-Layer Matching Search was the algorithm designed to reduce the access timing to the most accurate and similar pose position. The proposed algorithm was tested using facial range images from 130 people with normal facial expressions and without eyeglasses. The results achieved the mean success rate of 95.67 percent of
ER -