Regularized trace ratio discriminant analysis with patch distribution feature for human gait recognition

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.