An Efficient Convolutional Neural Network for Fingerprint Pore Detection
Bibliographic record
Abstract
Pore detection for fingerprint recognition has gained much research attention in recent years, in view of the existence of large number of pores in a small fingerprint segment and availability of high-resolution acquisition devices. Current research efforts have focused on developing two-part hybrid schemes, wherein the first part is comprised of a CNN architecture to produce a pore intensity map and the second part consists of a scheme that determines the pore centroids exploiting the knowledge base on the pores characteristics using this pore intensity map. However, CNN architectures used in the first part of the existing pore detection schemes are unable to extract pore features that adequately represent the fingerprints at a reasonable computational cost and in the second part the methods are not able to exploit the knowledge base on fingerprint pores efficiently. In this paper, a new two-part fingerprint pore detection scheme is proposed, wherein the first part focuses on developing a CNN architecture capable of extracting highly representational pore features and the second part on accurately determining the pore centroids by taking into consideration the inadequacies in fingerprint acquisition and distinguishing the spatial characteristics of true and false pores. Extensive experiments are performed to demonstrate the distinct characteristics to show the superiority of the proposed scheme in performance and complexity over the existing state-of-the-art pore detection schemes.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".