TY - GEN
T1 - Regularized trace ratio discriminant analysis with patch distribution feature for human gait recognition
AU - Huang, Yi
AU - Xu, Dong
AU - Nie, Feiping
PY - 2010
Y1 - 2010
N2 - We propose a new dimension reduction algorithm in combination with the Gaussian Mixture Model (GMM) based Patch Distribution Feature for human gait recognition. Instead of representing each average silhouette image as its gray-level feature, we first extract local patch features at every pixel of the average silhouette image and train a GMM to describe the distribution of the patches in each image. A Universal Background Model (UBM) is first trained with local patch features from all gallery images, then every gallery or probe image is represented by the distribution parameters (referred to as Patch Distribution Features (PDF)) of the image-specific GMM adapted from the UBM. To cope with the high dimension of the PDF feature, the Regularized Trace Ratio Discriminant Analysis (RTRDA) is developed to find the most discriminant subspaces for gait recognition. Experiments on USF humanID database show that RTRDA significantly outperforms the existing algorithms and achieves the best recognition results among all the previous works on USF humanID database in terms of average rank-1 recognition rate.
AB - We propose a new dimension reduction algorithm in combination with the Gaussian Mixture Model (GMM) based Patch Distribution Feature for human gait recognition. Instead of representing each average silhouette image as its gray-level feature, we first extract local patch features at every pixel of the average silhouette image and train a GMM to describe the distribution of the patches in each image. A Universal Background Model (UBM) is first trained with local patch features from all gallery images, then every gallery or probe image is represented by the distribution parameters (referred to as Patch Distribution Features (PDF)) of the image-specific GMM adapted from the UBM. To cope with the high dimension of the PDF feature, the Regularized Trace Ratio Discriminant Analysis (RTRDA) is developed to find the most discriminant subspaces for gait recognition. Experiments on USF humanID database show that RTRDA significantly outperforms the existing algorithms and achieves the best recognition results among all the previous works on USF humanID database in terms of average rank-1 recognition rate.
KW - Gaussian mixture model
KW - Human gait recognition
KW - Regularized trace ratio discriminant analysis
UR - https://www.scopus.com/pages/publications/78651102322
U2 - 10.1109/ICIP.2010.5651825
DO - 10.1109/ICIP.2010.5651825
M3 - 会议稿件
AN - SCOPUS:78651102322
SN - 9781424479948
T3 - Proceedings - International Conference on Image Processing, ICIP
SP - 2449
EP - 2452
BT - 2010 IEEE International Conference on Image Processing, ICIP 2010 - Proceedings
T2 - 2010 17th IEEE International Conference on Image Processing, ICIP 2010
Y2 - 26 September 2010 through 29 September 2010
ER -