Fingerprint Liveness Detection Using an Improved CNN With Image Scale Equalization
Bibliographic record
Abstract
Due to the lack of pre-judgment of fingerprints, fingerprint authentication systems are frequently vulnerable to artificial replicas. Anonymous people can impersonate authorized users to complete various authentication operations, thereby disrupting the order of life and causing tremendous economic losses to society. Therefore, to ensure that authorized users' fingerprint information is not used illegally, one possible anti-spoofing technique, called fingerprint liveness detection (FLD), has been exploited. Compared with the hand-crafted feature methods, the deep convolutional neural network (DCNN) can automatically learn the high-level semantic detail via supervised learning algorithm without any professional background knowledge. However, one disadvantage of most CNNs models is that fixed scale images (e.g., 227 × 227 ) are essential in the input layer. Although the scale problem can be handled by cropping or scaling operations via transforming an image of any scale into a fixed scale, they can easily cause some key texture information loss and image resolution degradation, which will weaken the generalization performance of the classifier model. In this paper, a novel FLD method called an improved DCNN with image scale equalization, has been proposed to preserve texture information and maintain image resolution. Besides, an adaptive learning rate method has been used in this paper. In the performance evaluation, the confusion matrix is applied into FLD for the first time as a performance indicator. The amounts of the experimental results based on the LivDet 2011 and LivDet 2013 data sets also verify that the detection performance of our method is superior to other methods.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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; 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".