An Analytical Study on the influence of using Trimmed Gait Energy Images for Human Gait Biometrics using Deep Learning
- Title
- An Analytical Study on the influence of using Trimmed Gait Energy Images for Human Gait Biometrics using Deep Learning
- Creator
- Poulose N.P.; Kt T.
- Description
- Gait based human recognition is founded on the principle that every human being has a distinctive style of walking. With the rise in the use of video surveillance devices, gait is one of the most convenient biometrics to use, in forensics. This paper is an analytical study of the effect of using trimmed Gait Energy Images (GEI) for Human Recognition using different deep learning techniques. Gait energy images are a spatiotemporal, silhouette-based representation of the human gait. GEIs from the CASIA B Multiview dataset was used to build two other sets of data by subtracting the upper body Deep learning and transfer learning techniques including Convolution Neural Networks (CNN) and VGG16 algorithms had been implemented to carry out the recognition. Results showed that the performance of the model using upper body images gives a greater accuracy than the lower body images. It has also been observed that the accuracy of recognition provided by the upper part of the body is almost the same as that achieved by the whole body, which brings forth the idea that the upper part of the body is the most pertinent in Human Identification using Gait as a biometric. 2022 IEEE.
- Source
- 2022 2nd International Conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2022, pp. 1266-1269.
- Date
- 2022-01-01
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Subject
- Biometric; Convolution neural networks; Gait Energy Images; Gait recognition; trimmed images
- Coverage
- Poulose N.P., Christ University, Bangalore, Department of Data Science, Bangalore, India; Kt T., Christ University, Bangalore, Department of Data Science, Bangalore, India
- Rights
- Restricted Access
- Relation
- ISBN: 978-166543789-9
- Format
- Online
- Language
- English
- Type
- Conference paper
Collection
Citation
Poulose N.P.; Kt T., “An Analytical Study on the influence of using Trimmed Gait Energy Images for Human Gait Biometrics using Deep Learning,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 25, 2025, https://archives.christuniversity.in/items/show/20271.