Hair Color Classification in Face Recognition using Machine Learning Algorithms
Bibliographic record
Abstract
Security through automatic human identification is critically important today, and this is largely due to the high volume of communications.Most methods used to identify individuals often use biometrics information, such as facial characteristics.Therefore, face recognition and classification have garnered great interest among computer vision researchers over the past decade.This pattern recognition problem is divided into several subcategories, such as eye or hair detection and classification.Hair is a salient feature in the human face and is one of the most important cues in face detection and recognition.Accurate detection and presentation of the hair region is one of the key components in the automatic synthesis of human facial caricature.In this work, hair color classification through feature extraction and machine learning methods was performed.The impacts of different features and classifiers were investigated using color samples.Support vector machines (SVM) and Kth nearest neighbors (K-NN) were trained by variety sets of statistical and color features, and the trained models were validated.Additionally, the effects of the size of datasets and feature dimensionality reduction were obtained.The best accuracy rate of 99% was achieved through a support vector machine with radial basis kernel function (SVM-RBF) using nine selected statistical and color features.
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How this classification was reachedexpand
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 | 0.033 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.013 |
| Science and technology studies | 0.004 | 0.016 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".