# Ordinal Distribution Regression for Gait-based Age Estimation

arXiv: https://arxiv.org/abs/1905.11005 · HTML: https://mlsanity.com/paper/1905.11005/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 May 2019.

## Built on by (most cited first, 4 of 4)

- [Meta Ordinal Regression Forest for Medical Image Classification with Ordinal Labels](https://mlsanity.com/paper/2203.07725/) (2022)
- [From Face to Gait: Weakly-Supervised Learning of Gender Information from Walking Patterns](https://mlsanity.com/paper/2111.00538/) (2021)
- [Gait-based Age Group Classification with Adaptive Graph Neural Network](https://mlsanity.com/paper/2210.00294/) (2022)
- [Age Range Estimation using MTCNN and VGG-Face Model](https://mlsanity.com/paper/2104.08585/) (2021)

## Abstract (arXiv)

Computer vision researchers prefer to estimate age from face images because facial features provide useful information. However, estimating age from face images becomes challenging when people are distant from the camera or occluded. A person's gait is a unique biometric feature that can be perceived efficiently even at a distance. Thus, gait can be used to predict age when face images are not available. However, existing gait-based classification or regression methods ignore the ordinal relationship of different ages, which is an important clue for age estimation. This paper proposes an ordinal distribution regression with a global and local convolutional neural network for gait-based age estimation. Specifically, we decompose gait-based age regression into a series of binary classifications to incorporate the ordinal age information. Then, an ordinal distribution loss is proposed to consider the inner relationships among these classifications by penalizing the distribution discrepancy between the estimated value and the ground truth. In addition, our neural network comprises a global and three local sub-networks, and thus, is capable of learning the global structure and local details from the head, body, and feet. Experimental results indicate that the proposed approach outperforms state-of-the-art gait-based age estimation methods on the OULP-Age dataset.