Advanced Age Group Estimation Using Gait Analysis: A Novel Multi-Energy Image and Invariant Moments Method

Abstract

The precise estimation of age is pivotal in identity verification across critical security checkpoints, including seaports, land borders, and airports. This study introduces an innovative methodology for age estimation based on gait analysis, enhancing security measures and providing reliable identity confirmation. Utilizing a novel preprocessing technique on gait datasets, this approach amalgamates three integral components: the Accumulated Frame Difference Energy Image (AFDEI), the Gait Energy Image (GEI), and the Invariant Moment of the image. These elements collectively facilitate the efficient extraction and analysis of critical gait data. Evaluation of the model, employing a Convolutional Neural Network (CNN), was conducted on the publicly available OU-ISRI with age dataset. The model demonstrated remarkable proficiency, achieving an average accuracy of 90.40% across 14 distinct view angles within a 5 K-Fold framework. This methodological advancement significantly outperforms existing state-of-the-art techniques in accuracy. The findings highlight the efficacy and potential of the proposed method for age grouping estimation through human gait analysis. Despite these advancements, it is crucial to acknowledge the study's limitations, particularly the dependency on silhouette images. Preprocessing is essential prior to implementing the proposed methodology. The outcomes of this study are instrumental in reinforcing age estimation as a key factor in bolstering identity verification processes at essential security junctures.