Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/3996
Title: VGG-16 based Gait Recognition using Skeleton Features
Authors: Ali, Irshad
Junaid, Iman
Ari, Samit
Keywords: Gait Recongnition
VGG-16
SkeGEI
Multilayer perceptron
Issue Date: Mar-2023
Citation: International Conference on Device Intelligence, Computing and Communication Technologies(DICCT), Dehradun, India, 17-18 March 2023
Abstract: Gait recognition is a new technology that can identify people with various walking patterns. Gait of a person is the manner in which they walk. A person’s walking stride is distinctive due to their body movements. Due to the popularity of the Kinect, human gait can be recognised to using 3D skeletal information. In this paper, Skeleton Gait Energy(SkeGEI) is image-based approach for gait recognition and VGG-16 with multilayer perceptron is proposed to effectively exploit raw depth information collected by the Kinect sensor. To restore as much gait data as possible, fine-tuned VGG-16 is used to extract space - time deep feature data from SkeGEI. Multilayer perceptron is then used to ascertain the connection between the corresponding subject and the gait features. Softmax is used for classification. Experiments on three different datasets show that our technique outclasses the majority of gait recognition methods.
Description: Copyright belongs to proceeding publisher
URI: http://hdl.handle.net/2080/3996
Appears in Collections:Conference Papers

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