Robust and Lightweight System for Gait-based Age Estimation towards Viewing Angle Variations

Abstract

Abstract: Gait based age recognition is a very challenging task, as it involves multiple hurdles such as change in 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 comparing the performance with state of 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. Background: In computer vision applications, gait-based age estimation across several cameras is critical, especially when following the same person in various viewpoints. Objective: To implement the system which adopts lightweight approach for gait-based age estimation. Method: 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. Result: The performance of the system is extensively evaluated on the OU-MVLP and OULP-Age datasets. Conclusion: The proposed system attains best mean absolute error (MAE) of 5.05 (in years) for the OU-MVLP dataset, and 5.65 for the OULP dataset.