# Gait-based Age Group Classification with Adaptive Graph Neural Network

arXiv: https://arxiv.org/abs/2210.00294 · HTML: https://mlsanity.com/paper/2210.00294/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 76 references that are in the map.
- First posted on arXiv 1 Oct 2022.

## Builds on (most cited first, 14 of 14)

- [Class-Balanced Loss Based on Effective Number of Samples](https://mlsanity.com/paper/1901.05555/) (2019)
- [Geometric deep learning: going beyond Euclidean data](https://mlsanity.com/paper/1611.08097/) (2016)
- [Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networks](https://mlsanity.com/paper/1810.02244/) (2018)
- [RMPE: Regional Multi-person Pose Estimation](https://mlsanity.com/paper/1612.00137/) (2016)
- [Design Space for Graph Neural Networks](https://mlsanity.com/paper/2011.08843/) (2020)
- [Rank consistent ordinal regression for neural networks with application to age estimation](https://mlsanity.com/paper/1901.07884/) (2019)
- [GaitGraph: Graph Convolutional Network for Skeleton-Based Gait Recognition](https://mlsanity.com/paper/2101.11228/) (2021)
- [JointsGait:A model-based Gait Recognition Method based on Gait Graph Convolutional Networks and Joints Relationship Pyramid Mapping](https://mlsanity.com/paper/2005.08625/) (2020)
- [Fine-Grained Age Estimation in the wild with Attention LSTM Networks](https://mlsanity.com/paper/1805.10445/) (2018)
- [Deep Differentiable Random Forests for Age Estimation](https://mlsanity.com/paper/1907.10665/) (2019)
- [Towards Speaker Age Estimation with Label Distribution Learning](https://mlsanity.com/paper/2202.11424/) (2022)
- [Multimodal Age and Gender Classification Using Ear and Profile Face Images](https://mlsanity.com/paper/1907.10081/) (2019)
- [Ordinal Distribution Regression for Gait-based Age Estimation](https://mlsanity.com/paper/1905.11005/) (2019)
- [Age and Gender Classification From Ear Images](https://mlsanity.com/paper/1806.05742/) (2018)

## Built on by (most cited first, 2 of 2)

- [Quantifying Jump Height Using Markerless Motion Capture with a Single Smartphone](https://mlsanity.com/paper/2302.10749/) (2023)
- [Using simulation to calibrate real data acquisition in veterinary medicine](https://mlsanity.com/paper/2307.11695/) (2023)

## Abstract (arXiv)

Deep learning techniques have recently been utilized for model-free age-associated gait feature extraction. However, acquiring model-free gait demands accurate pre-processing such as background subtraction, which is non-trivial in unconstrained environments. On the other hand, model-based gait can be obtained without background subtraction and is less affected by covariates. For model-based gait-based age group classification problems, present works rely solely on handcrafted features, where feature extraction is tedious and requires domain expertise. This paper proposes a deep learning approach to extract age-associated features from model-based gait for age group classification. Specifically, we first develop an unconstrained gait dataset called Multimedia University Gait Age and Gender dataset (MMU GAG). Next, the body joint coordinates are determined via pose estimation algorithms and represented as compact gait graphs via a novel part aggregation scheme. Then, a Part-AdaptIve Residual Graph Convolutional Neural Network (PairGCN) is designed for age-associated feature learning. Experiments suggest that PairGCN features are far more informative than handcrafted features, yielding up to 99% accuracy for classifying subjects as a child, adult, or senior in the MMU GAG dataset. These results suggest the feasibility of deploying Artificial Intelligence-enabled solutions for access control, surveillance, and law enforcement in unconstrained environments.