Performance Analysis of ANN based Gait Recognition

Authors(3) :-Taniya Sharma, Satnam Singh Dub, Bhanu Gupta

It is well-known that biometrics are a powerful tool for reliable automated person identification. Automatic gait recognition is one of the newest of the emergent biometrics and has many advantages over other biometrics. The most notable advantage is that it does not require contact with the subjects nor does it require the subject to be near a camera. This work employs a gait recognition process with binary silhouette-based input images and Artificial Neural Network (ANN) based classification in MATLAB. The performance of the recognition method depends significantly on the quality of the extracted binary silhouettes. In this work, a computationally low-cost fuzzy correlogram based method is employed for background subtraction. Even highly robust background subtraction and shadow elimination algorithms produce erroneous outputs at times with missing body portions, which consequently affect the recognition performance. Frame Difference Energy Image (FDEI) reconstruction is performed to alleviate the detrimental effect of improperly extracted silhouettes and to make the recognition method robust to partial incompleteness. Subsequently, features are extracted via two methods and fed to the BPNN (Back Propagation Neural Network based classifier which uses feature vector (exemplars) to compute similarity scores and carry out identification using weight vectors i.e. Frame-to-Exemplar-Distance (FED) vector. The FED uses the distance measure between pre-determined feature vectors and the weight vectors of the current frame as an identification criterion. The ANN performance is evaluated for recognition and speed parameters at different training gait angles.

Authors and Affiliations

Taniya Sharma
Department of ECE, SSCET, IKGPTU, Pathankot, Punjab, India
Satnam Singh Dub
Department of ECE, SSCET, IKGPTU, Pathankot, Punjab, India
Bhanu Gupta
Department of AE&IE, M.B.S.C.E.T, University of Jammu, Jammu, J&K, India

Gait, Biometrics, ANN, FDEI, FED, MATLAB

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Publication Details

Published in : Volume 4 | Issue 11 | November-December 2018
Date of Publication : 2018-12-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 112-125
Manuscript Number : IJSRST18401123
Publisher : Technoscience Academy

Print ISSN : 2395-6011, Online ISSN : 2395-602X

Cite This Article :

Taniya Sharma, Satnam Singh Dub, Bhanu Gupta, " Performance Analysis of ANN based Gait Recognition", International Journal of Scientific Research in Science and Technology(IJSRST), Print ISSN : 2395-6011, Online ISSN : 2395-602X, Volume 4, Issue 11, pp.112-125, November-December-2018. Available at doi :
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