A Comprehensive Method for Fingerprint Classification Based on Gabor Filters and Machine Learning
Bibliographic record
Abstract
The fingerprint is a valuable tool for both forensic analysis and community security.Stateof-the-art fingerprint classification methods tend to ignore image quality enhancement as well as use high-dimension feature sets resulting in unnecessary computational complexities.To address these issues, this study proposes an efficient fingerprint classification method that combines Histogram of Oriented Gradient (HOG) and Gabor Filter features with Random Forest (RF) and Naï ve Bayes (NAÏ VE) classifiers.It sequentially preprocesses the input with a series of receiving functions that enhance the image, such as grayscale, morphological, and binary.The method's performance was evaluated on the SOCOFing dataset, and 99% classification accuracy was demonstrated using the Gabor-Naï ve approach, surpassing some sophisticated techniques in terms of accuracy and computational efficiency.This work contributes to the field by addressing gaps in image enhancement and feature dimensionality, offering a robust solution for authenticating and distinguishing altered fingerprints.Future research could build on this by examining different classifiers for additional optimization and testing the methodology on a variety of datasets.
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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 | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".