Version 1
: Received: 30 May 2023 / Approved: 2 June 2023 / Online: 5 June 2023 (08:12:12 CEST)
How to cite:
Al Musalhi, N.; Çelebi, E. Age Estimation in Human Gait Extraction using A Combination of Multi-Energy Image with Invariant Moment. Preprints2023, 2023060186. https://doi.org/10.20944/preprints202306.0186.v1
Al Musalhi, N.; Çelebi, E. Age Estimation in Human Gait Extraction using A Combination of Multi-Energy Image with Invariant Moment. Preprints 2023, 2023060186. https://doi.org/10.20944/preprints202306.0186.v1
Al Musalhi, N.; Çelebi, E. Age Estimation in Human Gait Extraction using A Combination of Multi-Energy Image with Invariant Moment. Preprints2023, 2023060186. https://doi.org/10.20944/preprints202306.0186.v1
APA Style
Al Musalhi, N., & Çelebi, E. (2023). Age Estimation in Human Gait Extraction using A Combination of Multi-Energy Image with Invariant Moment. Preprints. https://doi.org/10.20944/preprints202306.0186.v1
Chicago/Turabian Style
Al Musalhi, N. and Erbuğ Çelebi. 2023 "Age Estimation in Human Gait Extraction using A Combination of Multi-Energy Image with Invariant Moment" Preprints. https://doi.org/10.20944/preprints202306.0186.v1
Abstract
Accurately estimating a person's age is crucial for identity verification at all critical checkpoints, including airports, land borders, and seaports. Human gait may be used as a biometric identifier and indicator of age, among other things. This study aims to develop methods for estimating a person's age by observing their walk. In this paper, a novel technique for preprocessing the proposed gait dataset has been used by utilizing a combination of the gait energy image (GEI), the accumulated frame difference energy image (AFDEI), and the invariant moment of the image. The proposed technique outperformed state-of-the-art methods in terms of accuracy. The proposed method was tested and evaluated using a convolutional neural network (CNN), and it achieved an average accuracy of 90.35% across 14 different view angles within 5 K-Fold, the proposed method resulted in 94.68% and 94.54% in 30º and 75º view degree respectively. concluding that the approach is effective and promising for estimating age using human gait.
Keywords
age estimation; AFDEI; CNN; human gait; neural networks; GEI; invariant moments
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
