TY - GEN
T1 - Gait recognition using sampled point vectors
AU - Hong, Sungjun
AU - Lee, Heesung
AU - Oh, Kyongsae
AU - Park, Mignon
AU - Kim, Euntail
PY - 2006
Y1 - 2006
N2 - Gait is a new biometric aimed to recognize individuals by the way they walk. Gait recognition has recently an increasing interest from researchers due to several advantages. In this paper, we have proposed a new feature vector, sampled point vector, for gait recognition based on model-free method. We choose the mean and variance of value of pixels which are sampled along to central axis of silhouette image for several frames. In contract to other system, proposed features are very simple and require low storages. Nevertheless, experimental result show sufficiently good performance. To evaluate, we use a reduced multivariate model as a classifier.
AB - Gait is a new biometric aimed to recognize individuals by the way they walk. Gait recognition has recently an increasing interest from researchers due to several advantages. In this paper, we have proposed a new feature vector, sampled point vector, for gait recognition based on model-free method. We choose the mean and variance of value of pixels which are sampled along to central axis of silhouette image for several frames. In contract to other system, proposed features are very simple and require low storages. Nevertheless, experimental result show sufficiently good performance. To evaluate, we use a reduced multivariate model as a classifier.
UR - http://www.scopus.com/inward/record.url?scp=34250738404&partnerID=8YFLogxK
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U2 - 10.1109/SICE.2006.314931
DO - 10.1109/SICE.2006.314931
M3 - Conference contribution
AN - SCOPUS:34250738404
SN - 8995003855
SN - 9788995003855
T3 - 2006 SICE-ICASE International Joint Conference
SP - 3937
EP - 3940
BT - 2006 SICE-ICASE International Joint Conference
T2 - 2006 SICE-ICASE International Joint Conference
Y2 - 18 October 2006 through 21 October 2006
ER -