Textured Contact Lenses Detection in Iris Recognition Using Weber Local Descriptor (WLD)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Out of many available biometric identification methods, iris recognition seems to be promising and most accurate method. The reason is iris structure remains unchanged throughout one's lifetime. One of the live applications of this is: over 1000 ATMs of financial institutions in Chicago and Montreal are now using iris recognition in lieu of debit cards. Imagine the situation if iris recognition systems/scans used in ATMs are fooled or spoofed. Financial system will break with a huge damage. To avoid this, there must exist technique(s) to determine if iris recognition methods are being bypassed. This paper presents an in-depth analysis of the effect of contact lens on iris recognition performance. We also present the IIIT-D Contact Lens Iris database with over 6500 images pertaining to 101 subjects. For each subject, images are captured without lens, transparent (prescription) lens, and color cosmetic lens (textured) using two different iris sensors. Weber Local Descriptor (WLD) is proposed in this paper for feature extraction in contact lenses detection. Also, the results are compared with Binarized Statistical Image Feature (BSIF) analysis which shows that WLD gives favorable results. We organize WLD features to compute a histogram by encoding both differential excitations and orientations at certain locations. This method focuses on different properties of a pixel of iris image and thus, it provides more accurate results than other techniques.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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 it