Abstract
Gait pattern classification is a way of classifying the human walking pattern for identification purposes. The paper aims to compare different classifiers for gait-based human identification systems with covariate conditions. For the experimental analysis, we used the CASIA-B dataset, GEI for gait feature representation, and the HOG feature descriptor for feature extraction. The discriminant function for classification is chosen using linear discriminant analysis. The feature vectors are fed into different classical classifiers such as support vector machine (SVM), random forest (RF), k-nearest-neighbors (KNN) and nearest centroid classification (NC). From the experimental results, we proposed that the nearest centroid classification model is an effective classifier for gait pattern classification covering viewing covariates and appearance change from carrying and clothing covariates.
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Kathing, M., Brahma, R.K., Saharia, S. (2022). Comparative Study on Different Classifiers for Gait-Based Human Identification. In: Gupta, D., Sambyo, K., Prasad, M., Agarwal, S. (eds) Advanced Machine Intelligence and Signal Processing. Lecture Notes in Electrical Engineering, vol 858. Springer, Singapore. https://doi.org/10.1007/978-981-19-0840-8_10
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DOI: https://doi.org/10.1007/978-981-19-0840-8_10
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