Improving single view gait recognition using sparse representation based classification

This paper explores a better way of recognition using sparse based representation, by taking into account a number of covariates that affect single view based gait. Nevertheless, the conventional methods couldn't handle covariates effectively. Our propose framework comprises a dictionary, which...

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Bibliographic Details
Published inTechSym 2016 : 2016 IEEE Students' Technology Symposium : 30 September-2 October 2016, IIT Kharagpur pp. 317 - 321
Main Authors Das, Sonia, Sahoo, Upanedra Kumar, Meher, Sukadev
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.09.2016
Subjects
Covariance matrices
Dictionaries
dictionary
Feature extraction
Gait
Gait recognition
Histograms
l 1 -minimization
Legged locomotion
sparse representation
Training
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DOI10.1109/TechSym.2016.7872703

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Summary:This paper explores a better way of recognition using sparse based representation, by taking into account a number of covariates that affect single view based gait. Nevertheless, the conventional methods couldn't handle covariates effectively. Our propose framework comprises a dictionary, which describes five segments of a subject over a gait period. The feature vectors are educed from ellipse based parameters from each segments and fused to form a covariance matrix. Each matrix is used as dictionary atom and solved using - l 1 - minimization. The linear representations of sparse codes of different atoms are used for recognition. The proposed method is compared with that of state-of-the-art methods.
DOI:10.1109/TechSym.2016.7872703