Individual recognition using gait energy image.
In this paper, we propose a new spatio-temporal gait representation, called Gait Energy Image (GEI), to characterize human walking properties for individual recognition by gait. To address the problem of the lack of training templates, we also propose a novel approach for human recognition by combining statistical gait features from real and synthetic templates. We directly compute the real templates from training silhouette sequences, while we generate the synthetic templates from training sequences by simulating silhouette distortion. We use a statistical approach for learning effective features from real and synthetic templates. We compare the proposed GEI-based gait recognition approach with other gait recognition approaches on USF HumanID Database. Experimental results show that the proposed GEI is an effective and efficient gait representation for individual recognition, and the proposed approach achieves highly competitive performance with respect to the published gait recognition approaches.
Center for Research in Intelligent Systems, University of California, Riverside, 900 University Avenue, Riverside, CA 92521, USA. jhan@cris.ucr.edu
Ju
J
Han
Bir
B
Bhanu
D000465:Algorithms; D001185:Artificial Intelligence; D001699:Biometry / Q000379:methods*; D005684:Gait; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D016247:Information Storage and Retrieval / Q000379:methods; D007596:Joints / Q000033:anatomy & histology* / Q000502:physiology*; D008954:Models, Biological; D010363:Pattern Recognition, Automated / Q000379:methods*; D010781:Photography / Q000379:methods; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014741:Video Recording / Q000379:methods*