Impact of Using Partial Gait Energy Images for Human Recognition by Gait Analysis
Behavior biometrics; Convolution neural network; Deep learning; Gait analysis; Gait energy image; Human recognition; Partial GEI; PCA; Random forest; Support vector machine; Upper body GEI
Gait analysis is a behavioral biometric that classifies human, based on how they walk and other variables involved in the forward movement. In this study, we have attempted to comprehend the significance of the upper portion of the body in gait analysis for human recognition. The data for this study came from the CASIA dataset, which was donated by the Chinese Academy of Sciences Institute of Automation. We began by extracting the gait energy image (GEI) from the dataset and employing principal component analysis to minimize the dimensionality (PCA). For classification, random forest, support vector machine (SVM), and convolution neural network (CNN) algorithms are implemented to recognize the human subjects. This paper provides experimental results to show the accuracy attained when classification is done on GEI of full-body images is higher than the accuracy attained when classification is done on GEI of the lower portion of the body only. It also shows the significance of the GEI of the upper portion of the body. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Lecture Notes in Electrical Engineering, Vol-947, pp. 175-185.
Springer Science and Business Media Deutschland GmbH
Singh D., Christ (Deemed to be) University, Pune, India; Thomas K.T., Christ (Deemed to be) University, Pune, India