Gait Recognition by Cross Wavelet Transform and Graph Model
| Autor: | P. J. Deore, Sagar Arun More |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Normalization (statistics)
Computer science business.industry Feature vector Feature extraction Wavelet transform 020206 networking & telecommunications Pattern recognition 02 engineering and technology Similarity measure Quadrature mirror filter Euclidean distance Artificial Intelligence Control and Systems Engineering 0202 electrical engineering electronic engineering information engineering Bipartite graph 020201 artificial intelligence & image processing Artificial intelligence business Information Systems |
| Zdroj: | IEEE/CAA Journal of Automatica Sinica. 5:718-726 |
| ISSN: | 2329-9274 2329-9266 |
| DOI: | 10.1109/jas.2018.7511081 |
| Popis: | 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 U+0028 QMF U+0029-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 U+0025 average recognition rate. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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