Age estimation from facial images based on Gabor feature fusion and the CIASO‐SA algorithm
Bibliographic record
Abstract
Abstract Aiming at the problem of long time‐consuming and low accuracy of existing age estimation approaches, a new age estimation method using Gabor feature fusion, and an improved atomic search algorithm for feature selection is proposed. Firstly, texture features of five scales and eight directions in the face region are extracted by Gabor wavelet transform. The statistical histogram is introduced to encode and fuse the directional index with the largest feature value on Gabor scales. Secondly, a new hybrid feature selection algorithm chaotic improved atom search optimisation with simulated annealing (CIASO‐SA) is presented, which is based on an improved atomic search algorithm and the simulated annealing algorithm. Besides, the CIASO‐SA algorithm introduces a chaos mechanism during atomic initialisation, significantly improving the convergence speed and accuracy of the algorithm. Finally, a support vector machine (SVM) is used to get classification results of the age group. To verify the performance of the proposed algorithm, face images with three resolutions in the Adience dataset are tested. Using the Gabor real part fusion feature at 48 × 48 resolution, the average accuracy and 1‐off accuracy of age classification exhibit a maximum of 60.4% and 85.9%, respectively. Obtained results prove the superiority of the proposed algorithm over the state‐of‐the‐art methods, which is of great referential value for application to the mobile terminals.
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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 | 0.001 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".