Deep Learning Based Gender Identification Using Ear Images
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
The classification of an individual as male or female is a significant issue with several practical implications.In recent years, automatic gender identification has garnered considerable interest because of its potential applications in e-commerce and the accumulation of demographic data.Recent observations indicate that models based on deep learning have attained remarkable success in a variety of problem domains.In this study, our aim is to establish an end-to-end model that capitalizes on the strengths of competing convolutional neural network (CNN) and vision transformer (ViT) models.To accomplish this, we propose a novel approach that combines the MobileNetV2 model, which is recognized for having fewer parameters than other CNN models, with the ViT model.Through rigorous evaluations, we have compared our proposed model with other recent studies using the accuracy metric.Our model attained state-of-the-art performance with a remarkable score of 96.66% on the EarVN1.0dataset, yielding impressive results.In addition, we provide t-SNE results that demonstrate our model's superior learning representation.Notably, the results show a more effective disentanglement of classes.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.000 | 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