11K Hands: Gender recognition and biometric identification using a large\n dataset of hand images
Bibliographic record
Abstract
The human hand possesses distinctive features which can reveal gender\ninformation. In addition, the hand is considered one of the primary biometric\ntraits used to identify a person. In this work, we propose a large dataset of\nhuman hand images (dorsal and palmar sides) with detailed ground-truth\ninformation for gender recognition and biometric identification. Using this\ndataset, a convolutional neural network (CNN) can be trained effectively for\nthe gender recognition task. Based on this, we design a two-stream CNN to\ntackle the gender recognition problem. This trained model is then used as a\nfeature extractor to feed a set of support vector machine classifiers for the\nbiometric identification task. We show that the dorsal side of hand images,\ncaptured by a regular digital camera, convey effective distinctive features\nsimilar to, if not better, those available in the palmar hand images. To\nfacilitate access to the proposed dataset and replication of our experiments,\nthe dataset, trained CNN models, and Matlab source code are available at\n(https://goo.gl/rQJndd).\n
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.017 |
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".