Identification of humans using gait

TitleIdentification of humans using gait
Publication TypeJournal Articles
Year of Publication2004
AuthorsKale A, Sundaresan A, Rajagopalan AN, Cuntoor NP, Roy-Chowdhury AK, Kruger V, Chellappa R
JournalImage Processing, IEEE Transactions on
Pagination1163 - 1173
Date Published2004/09//
ISBN Number1057-7149
KeywordsAutomated;Reproducibility of Results;Sensitivity and Specificity;Signal Processing, binary silhouette;frame to exemplar distance;gait databases;gait recognition;hidden Markov model;human identification;image features;observation probability;observation vector;hidden Markov models;image recognition;image representation;image sequences;pro, Biological;Models, Computer-Assisted;Models, Computer-Assisted;Subtraction Technique;Task Performance and Analysis;Video Recording;, Statistical;Pattern Recognition

We propose a view-based approach to recognize humans from their gait. Two different image features have been considered: the width of the outer contour of the binarized silhouette of the walking person and the entire binary silhouette itself. To obtain the observation vector from the image features, we employ two different methods. In the first method, referred to as the indirect approach, the high-dimensional image feature is transformed to a lower dimensional space by generating what we call the frame to exemplar (FED) distance. The FED vector captures both structural and dynamic traits of each individual. For compact and effective gait representation and recognition, the gait information in the FED vector sequences is captured in a hidden Markov model (HMM). In the second method, referred to as the direct approach, we work with the feature vector directly (as opposed to computing the FED) and train an HMM. We estimate the HMM parameters (specifically the observation probability B) based on the distance between the exemplars and the image features. In this way, we avoid learning high-dimensional probability density functions. The statistical nature of the HMM lends overall robustness to representation and recognition. The performance of the methods is illustrated using several databases.