RESEARCH ARTICLE
Robust and Lightweight System for Gait-based Age Estimation towards Viewing Angle Variations
Jaychand Upadhyay1, *, Tad Gonsalves2, Vijay Katkar3
Article Information
Identifiers and Pagination:
Year: 2022Volume: 1
Issue: 2
E-location ID: e260822208023
Publisher ID: e260822208023
DOI: 10.2174/2666782701666220826104925
Article History:
Received Date: 31/03/2022Revision Received Date: 07/05/2022
Acceptance Date: 27/05/2022
Electronic publication date: 06/09/2022
Abstract
Background
In computer vision applications, gait-based age estimation across several cameras is critical, especially when following the same person from various viewpoints.
Introduction
Gait-based age recognition is a very challenging task as it involves multiple hurdles, such as a change in the viewpoint of the person. The proposed system handles this problem by performing a sequence of tasks, such as GEI formation from silhouette, applying DCT on GEI and extracting the features and finally using MLP for age estimation. The proposed system proves its effectiveness by comparing the performance with state-of-the-art methods, conventional methods and deep learning-based methods. The performance of the system is estimated on OU-MVLP and OULP-Age datasets. The experimental results show the robustness of the system against viewing angle variations.
Objective
This study aimed to implement the system, which adopts a lightweight approach for gait-based age estimation.
Methods
The proposed system uses a combination of the discrete cosine transform (DCT) and multi-layer perceptron (MLP) on gait energy image (GEI) to perform age estimation.
Results
The performance of the system is extensively evaluated on the OU-MVLP and OULP-Age datasets.
Conclusion
The proposed system attains the best mean absolute error (MAE) of 5.05 (in years) for the OU-MVLP dataset and 5.65 for the OULP dataset.
