Human Gait Recognition using Discrete Wavelet and Discrete Cosine and Transformation Based Features
|© 2021 by IJCTT Journal|
|Year of Publication : 2021|
|Authors : Abhishek Madduri|
|DOI : 10.14445/22312803/IJCTT-V69I6P104|
How to Cite?
Abhishek Madduri, "Human Gait Recognition using Discrete Wavelet and Discrete Cosine and Transformation Based Features," International Journal of Computer Trends and Technology, vol. 69, no. 6, pp. 22-27, 2021. Crossref, https://doi.org/10.14445/22312803/IJCTT-V69I6P104
Nowadays, human gait recognition is a popular technique due to security requirements in public places. Gait recognition technique is used to identify a person from his/her walking cycle and cooperation of a human being is not required in this process. In this article, the DWT (Discrete Wavelet Transform) and DCT (Discrete Cosine Transform) feature extraction techniques are considered for extracting the unique properties for gait recognition of an individual. For classification, DT (Decision Tree), RF (Random Forest) and K-NN (KNearest Neighbors) are considered in this work, because these classifiers are performing well in the field of pattern recognition research area. The gait cycle has two phases, namely, stance phase and swing phase. Stance phase has included heel strike, foot flat, mid stance, heel off, toe off. Swing phase included acceleration, mid swing, deacceleration. These both phases are considered in this work to recognize gait of an individual. Information of gait is obtained from different parts of silhouettes. The human silhouette is segmented into seven components namely head, arm, trunk, thigh, front leg, back leg, and foot. People can be recognized by their gait become popular and there are the various reasons such as this can be done remotely, do not need high resolution videos etc. In this article, the authors have considered publicly available dataset, namely CASIA-A gait image dataset for the experimental work. Using features and classification methods considered in this work, the authors have achieved a recognition accuracy of 84.26% with random forest classifier for CASIA-A public dataset. Significance of the work — In this article, the authors have presented DCT and DWT feature extraction techniques and decision tree, random forest and K-NN classification techniques for gait recognition of an individual. The authors have reported a recognition accuracy of 84.26% for CASIA-A public dataset of gait recognition using DCT features and Random Forest classifier.
DCT, DWT, Decision Tree, GAIT, k-NN, Random Forest.
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