Artificial Neural Network-Based Fingerprint Classification and Recognition
Bibliographic record
Abstract
The most commonly used biometric technique for identifying people is fingerprint-based biometrics. It is divided into two parts: verification (if this individual is genuinely himself) and identification (identifying a person from a pool of persons). Due to the enormous number of comparisons required, the Automatic Fingerprint Identification System (AFIS), which typically conducts two stages: feature extraction and matching, had difficulties with a large database of fingerprint photos for the real-time application. So, more classification stages for complete fingerprint data can make it faster for the AFIS to identify a person. In this paper, we presented a classification method for identifying detailed fingerprint information by utilizing a deep learning approach to support the operations for classifying, identifying, and recognising the fingerprint. The proposed method was designed to differentiate certain fingerprint information, such as left-right hand classification, sweatpore classification, scratch classification, and finger classification. We privately created our fingerprint image dataset due to high personalization and security concerns (25 fingerprint images in the dataset with seven features for each image through the scanning technique). Finally, the research results for the proposed study were accurate and outperformed previous results.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".