Gait identification with partial occlusion using six modules and consideration of occluded module exclusion

In gait identification, partial occlusion sometimes occurs and leads to missed identification. This paper proposes a gait identification method for partial occlusion case by using six modules and consideration of occluded module exclusion. In this method, a Gait Energy Image (GEI) is separated into four individual modules, and three of neighboring modules are coupled into other two coupling modules. When partial occlusion of a module occurs, the occluded module is detected and excluded from consideration for gait identification. In addition, the combined TDPCA and TDLDA are employed to extract gait features comparing with trained features in the database, and the candidate with the highest score in matching with the database is selected as identified person. To evaluate performance of proposed method, experiments carried out with CASIA dataset with 123 classes and our own EEPIT dataset with 135 classes indicate effectiveness of module separation and significance of exclusion of occluded modules.