TY - GEN
T1 - Multi-view gait recognition based on motion regression using multilayer perceptron
AU - Kusakunniran, Worapan
AU - Wu, Qiang
AU - Zhang, Jian
AU - Li, Hongdong
PY - 2010
Y1 - 2010
N2 - It has been shown that gait is an efficient biometric feature for identifying a person at a distance. However, it is a challenging problem to obtain reliable gait feature when viewing angle changes because the body appearance can be different under the various viewing angles. In this paper, the problem above is formulated as a regression problem where a novel View Transformation Model (VTM) is constructed by adopting Multilayer Perceptron (MLP) as regression tool. It smoothly estimates gait feature under an unknown viewing angle based on motion information in a well selected Region of Interest (ROI) under other existing viewing angles. Thus, this proposal can normalize gait features under various viewing angles into a common viewing angle before gait similarity measurement is carried out. Encouraging experimental results have been obtained based on widely adopted benchmark database.
AB - It has been shown that gait is an efficient biometric feature for identifying a person at a distance. However, it is a challenging problem to obtain reliable gait feature when viewing angle changes because the body appearance can be different under the various viewing angles. In this paper, the problem above is formulated as a regression problem where a novel View Transformation Model (VTM) is constructed by adopting Multilayer Perceptron (MLP) as regression tool. It smoothly estimates gait feature under an unknown viewing angle based on motion information in a well selected Region of Interest (ROI) under other existing viewing angles. Thus, this proposal can normalize gait features under various viewing angles into a common viewing angle before gait similarity measurement is carried out. Encouraging experimental results have been obtained based on widely adopted benchmark database.
UR - http://www.scopus.com/inward/record.url?scp=78149481382&partnerID=8YFLogxK
U2 - 10.1109/ICPR.2010.535
DO - 10.1109/ICPR.2010.535
M3 - Conference contribution
SN - 9780769541099
T3 - Proceedings - International Conference on Pattern Recognition
SP - 2186
EP - 2189
BT - Proceedings - 2010 20th International Conference on Pattern Recognition, ICPR 2010
T2 - 2010 20th International Conference on Pattern Recognition, ICPR 2010
Y2 - 23 August 2010 through 26 August 2010
ER -