Advanced Age Group Estimation Using Gait Analysis: A Novel Multi-Energy Image and Invariant Moments Method
Bibliographic record
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".