# Ordinal Distribution Regression for Gait-based Age Estimation

## TL;DR

This paper introduces an ordinal distribution regression approach using a global and local CNN for gait-based age estimation, effectively capturing age ordinal relationships and improving accuracy over existing methods.

## Contribution

It proposes a novel ordinal distribution regression framework with a combined global and local CNN architecture for more accurate gait-based age estimation.

## Key findings

- Outperforms state-of-the-art methods on OULP-Age dataset
- Effectively models ordinal age relationships
- Utilizes global and local features for improved accuracy

## Abstract

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.