TY - GEN
T1 - Cross-view gait-based gender classification by transfer learning
AU - Yao, Zhenjun
AU - Zhang, Zhaoxiang
AU - Hu, Maodi
AU - Wang, Yunhong
PY - 2013
Y1 - 2013
N2 - The gender of a person is easily recognized by his/her gait when training data and test data are from the same view. However, when it comes to cross-view gender classification, traditional methods can not deal with large view variation without enough labeled data in the target view. In this paper, we solve this problem by introducing a transfer learning based framework. Firstly, Gait Energy Image (GEI) of each gait sequence for all views is generated, and Principal Component Analysis (PCA) is carried out to obtain efficient gait representations. Subsequently, an inductive transfer learning approach, TrAdaBoost, is adopted to transfer knowledge from the source view to the target view, which significantly improves the performance of gait-based gender classification. Abundant experiments are conducted and experimental results demonstrate the superiority of the proposed method over traditional gait analysis methods.
AB - The gender of a person is easily recognized by his/her gait when training data and test data are from the same view. However, when it comes to cross-view gender classification, traditional methods can not deal with large view variation without enough labeled data in the target view. In this paper, we solve this problem by introducing a transfer learning based framework. Firstly, Gait Energy Image (GEI) of each gait sequence for all views is generated, and Principal Component Analysis (PCA) is carried out to obtain efficient gait representations. Subsequently, an inductive transfer learning approach, TrAdaBoost, is adopted to transfer knowledge from the source view to the target view, which significantly improves the performance of gait-based gender classification. Abundant experiments are conducted and experimental results demonstrate the superiority of the proposed method over traditional gait analysis methods.
KW - Cross-view
KW - Gait-based gender classification
KW - Transfer learning
UR - https://www.scopus.com/pages/publications/84894192474
U2 - 10.1007/978-3-319-03731-8_8
DO - 10.1007/978-3-319-03731-8_8
M3 - 会议稿件
AN - SCOPUS:84894192474
SN - 9783319037301
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 79
EP - 87
BT - Advances in Multimedia Information Processing, PCM 2013 - 14th Pacific-Rim Conference on Multimedia, Proceedings
PB - Springer Verlag
T2 - 14th Pacific-Rim Conference on Multimedia, PCM 2013
Y2 - 13 December 2013 through 16 December 2013
ER -