Facial age estimation using clustered multi-task support vector regression machine
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Automatic age estimation is the process of using a computer to predict the age of a person automatically based on a given facial image. While this problem has numerous real-world applications, the high variabil-ity of aging patterns and the sparsity of available data present challenges for model training. Here, instead of training one global aging function, we train an individ-ual function for each person by a multi-task learning approach so that the variety of human aging processes can be modelled. To deal with the sparsity of train-ing data, we propose a similarity measure for clustering the aging functions. During the testing stage, which in-volves a new person with no data used for model train-ing, we propose a feature-based similarity measure for characterizing the test case. We conduct simulation ex-periments on the FG-NET and MORPH databases and compared our method with other state-of-the-art meth-ods. 1
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 it