Learning age from gait: A survey

Timilehin B. Aderinola, Tee Connie, Thian Song Ong, Wei Chuen Yau, Andrew Beng Jin Teoh

Research output: Contribution to journalReview articlepeer-review

8 Citations (Scopus)


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.

Original languageEnglish
Article number9477552
Pages (from-to)100352-100368
Number of pages17
JournalIEEE Access
Publication statusPublished - 2021

Bibliographical note

Funding Information:
This work was supported by Xiamen University Malaysia Research Fund (Grant No. XMUMRF/2019-C4/IECE/0011) and Multimedia University Graduate Research Assistant Scheme under Grant MMUI/190045.

Publisher Copyright:
© 2013 IEEE.

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Materials Science(all)
  • Engineering(all)
  • Electrical and Electronic Engineering


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