Gait Recognition by Cross Wavelet Transform and Graph Model

Sagar Arun More; Pramod Jagan Deore

doi:10.1109/JAS.2018.7511081

Volume 5 Issue 3

May 2018

IEEE/CAA Journal of Automatica Sinica

JCR Impact Factor: 15.3, Top 1 (SCI Q1)

CiteScore: 23.5, Top 2% (Q1)
Google Scholar h5-index: 77， TOP 5

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Article Navigation > IEEE/CAA Journal of Automatica Sinica > 2018 > 5(3): 718-726

Sagar Arun More and Pramod Jagan Deore, "Gait Recognition by Cross Wavelet Transform and Graph Model," IEEE/CAA J. Autom. Sinica, vol. 5, no. 3, pp. 718-726, Mar. 2018. doi: 10.1109/JAS.2018.7511081

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Sagar Arun More and Pramod Jagan Deore, "Gait Recognition by Cross Wavelet Transform and Graph Model," IEEE/CAA J. Autom. Sinica, vol. 5, no. 3, pp. 718-726, Mar. 2018. doi: 10.1109/JAS.2018.7511081

Sagar Arun More and Pramod Jagan Deore, "Gait Recognition by Cross Wavelet Transform and Graph Model," IEEE/CAA J. Autom. Sinica, vol. 5, no. 3, pp. 718-726, Mar. 2018. doi: 10.1109/JAS.2018.7511081

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Gait Recognition by Cross Wavelet Transform and Graph Model

doi: 10.1109/JAS.2018.7511081

Department of Electronics and Telecommunication Engineering, R. C. Patel Institute of Technology, Shirpur 425405, India

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Abstract

Abstract

In this paper, a multi-view gait based human recognition system using the fusion of two kinds of features is proposed. We use cross wavelet transform to extract dynamic feature and bipartite graph model to extract static feature which are coefficients of quadrature mirror filter (QMF)-graph wavelet filter bank. Feature fusion is done after normalization. For normalization of features, min-max rule is used and mean-variance method is used to find weights for normalized features. Euclidean distance between each feature vector and center of the cluster which is obtained by k-means clustering is used as similarity measure in Bayesian framework. Experiments performed on widely used CASIA B gait database show that, the fusion of these two feature sets preserve discriminant information. We report 99.90% average recognition rate.
- Binary sequences,
- feature extraction,
- identification of persons,
- linear discriminant analysis (LDA)

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References

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Gait Recognition by Cross Wavelet Transform and Graph Model

doi: 10.1109/JAS.2018.7511081

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