Citation

Aderinola, Timilehin B. and Tee, Connie and Ong, Thian Song and Yau, Wei Chuen and Teoh, Andrew Beng Jin (2021) Learning Age From Gait: A Survey. IEEE Access, 9. pp. 100352-100368. ISSN 2169-3536

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Abstract

Age is an important human attribute that needs to be determined for various purposes, including security, health, human identification, and law enforcement. Hence, there is an increasing research interest in automatic age estimation using biometric traits such as face and gait. In recent years, gait analysis has received growing attention due to the pervasive nature of video surveillance. Gait signals that measure the manner of walking can be obtained using vision and sensor-based techniques. Individual gait patterns obtainable from videos, images, or sensors are shown unconsciously and are not easily obscured. Additionally, gait signals can be obtained unobtrusively with cameras placed at a long distance because gait does not require high-resolution images. However, the extraction of age-associated gait features is a challenging task due to various gait covariates. These covariates include clothing and view changes for vision-based gait; walking slope and footwear for sensor-based gait. This paper provides a survey of scientific literature on age estimation using gait features. We focus on the approaches to extracting age-associated gait features, namely, vision-based and sensor-based approaches, how they may be affected by the different covariates, and domain-specific applications. To make this work useful for as wide of an audience as possible, we also include discussions on key topics such as existing datasets, evaluation strategies, and open challenges that should be addressed in the future.

Item Type:	Article
Uncontrolled Keywords:	Biometric identification, Sensors, Legged locomotion, Cameras, Task analysis, Sensor phenomena and characterization
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7800-8360 Electronics
Divisions:	Faculty of Information Science and Technology (FIST)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	30 Aug 2021 10:35
Last Modified:	15 Mar 2023 02:30
URII:	http://shdl.mmu.edu.my/id/eprint/9452

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