Pith. sign in

REVIEW 4 major objections 6 minor 25 references

Smartphone-based Iris Recognition through High-Quality Visible Spectrum Iris Capture

T0 review · 4 major / 6 minor · reviewed 2026-08-11 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read A dedicated Android capture application enables a modern smartphone to acquire visible-spectrum iris images whose recognition accuracy approaches infrared-based iris systems, with reported true acceptance rates above 96% across spectra…

desk verdict A useful capture pipeline and a new public VIS iris dataset, but the headline TAR is computed in-sample and should not be trusted as a real-world accuracy estimate. read the letter →

arxiv 2412.13063 v1 pith:GKO77XPI submitted 2024-12-17 eess.IV cs.CV

Naveenkumar G Venkataswamy , Yu Liu , Surendra Singh , Soumyabrata Dey , Stephanie Schuckers , Masudul H Imtiaz This is my paper · ORCID
classification eess.IVcs.CV
keywords irisrecognitionvisiblespectrumsmartphonebiometricsCUVIRISdatasetG-ATTU-NetYOLOv3-tinyISO/IEC29794-6cross-spectralmatching
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper argues that a modern smartphone, with a purpose-built capture application, can acquire visible-spectrum iris images good enough for reliable biometric recognition, including matching against templates enrolled with near-infrared scanners. To that end the authors built an Android app that detects the eye and iris with a compact YOLOv3-tiny model, automatically adjusts focus and zoom, applies ISO/IEC 29794-6 quality checks, and segments the iris on-device with a lightweight G-ATTU-Net. They collected a new dataset, CUVIRIS, with visible and infrared iris images from 47 subjects, and report true acceptance rates of 96.57% (visible), 97.95% (infrared), and 96.17–96.31% for infrared-to-visible cross-spectral matching at a false acceptance rate of 0.01%. If these figures hold beyond the study's small, in-sample evaluation, the practical consequence is that iris recognition could run on ordinary phone cameras without infrared hardware, using existing NIR enrollments for verification.

What carries the argument

The load-bearing mechanism is the capture-and-feedback loop in the Android application: a YOLOv3-tiny detector (a compact 24-layer object detector) locates the eye and iris in each frame, the app scales those coordinates back to the sensor image and continuously adjusts camera zoom from the eye-box width and focal point from the iris coordinates, then crops a 640×480 eye image. The cropped frame must pass two quality gates — an OpenCV sharpness check (Laplacian and FFT) and the ISO/IEC 29794-6 metrics such as iris-pupil contrast, concentricity, margin adequacy, and usable iris area — before being saved. For recognition, the iris is segmented by G-ATTU-Net, a U-Net variant that replaces standard convolutions with Ghost Modules (which generate feature maps by combining a standard convolution with a depthwise separable convolution) and inserts attention blocks in the skip connections to keep the model at roughly 8 million parameters, about a quarter of the attention U-Net baseline. The segmented mask and red-channel image are normalized to 512×64 via the Hofbauer parameterization (rubber-sheet unwrapping with circular boundary detection), converted to a Gabor iris code by the OSIRIS v4.1 toolkit, and compared with masked Hamming distance including ±7 bit shifts for rotation alignment.

What would settle it

Run the identical capture-and-matching pipeline with the G-ATTU-Net weights frozen before any CUVIRIS images are used, the FAR threshold fixed in advance, and evaluation on a disjoint set of subjects; if the TAR at 0.01% FAR falls clearly below 96%, the paper's generalization claim is contradicted.

Watch

Extended reading notes

Core claim

The paper's central discovery claim is that high-resolution smartphone cameras, guided by a dedicated capture application, can produce visible-light iris images whose recognition accuracy approaches that of dedicated near-infrared imaging. Concretely, on the CUVIRIS dataset of 47 subjects captured with a Samsung Galaxy S21 Ultra, the authors report a true acceptance rate of 96.57% (left iris) and 96.81% (right iris) for visible-spectrum verification, 97.95% for NIR verification, and 96.17%/96.31% when NIR enrollment templates are verified against visible-spectrum probes, all at a false acceptance rate of 0.01%. Performance stayed above 96% across capture distances of 25 cm and 50 cm and across blue, brown, and gray iris colors. The authors attribute this outcome to the combination of automated focus and zoom on the iris, ISO/IEC 29794-6 quality gating, and an on-device segmentation model (G-ATTU-Net) that keeps the capture and matching pipeline free of the blur, reflection, and mis-segmentation artifacts that previously degraded VIS iris recognition.

Load-bearing premise

The load-bearing premise is that accuracy measured on the same 47 subjects used to fine-tune the segmentation and set the 0.01% FAR threshold predicts how the system will perform on people and conditions it has not seen.

Editorial extensions

If this is right

  • If the reported accuracy generalizes, iris recognition no longer requires specialized near-infrared cameras; a standard phone camera with the app could serve as the capture device.
  • The cross-spectral result implies that users enrolled on an existing NIR iris system could later verify on a smartphone in visible light, preserving prior enrollments.
  • The G-ATTU-Net's small parameter count and real-time speed suggest iris segmentation can run entirely on-device, keeping biometric templates local and reducing privacy exposure.
  • The ISO/IEC 29794-6 quality gating demonstrated here gives future mobile iris systems a concrete acceptance criterion that filters unusable frames before they enter the recognition pipeline.
  • CUVIRIS, once released, gives the research community a modern-smartphone VIS plus NIR benchmark to test new capture and matching methods on.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • An implication the paper leaves implicit: the same capture loop could be evaluated for other ocular biometrics, such as periocular or sclera recognition, where the automatic zoom and focus control would likely help as much as it does for the iris.
  • One extension we would suggest: the authors' in-sample evaluation could be stress-tested by reusing the exact app and matching pipeline on an independent cohort, which would clarify how much of the 96–98% TAR is due to the method versus the specific 47 subjects and tuned threshold.
  • A further consequence, if the cross-spectral numbers replicate: NIR-to-VIS matching could act as a fallback for law-enforcement or border databases built on infrared enrollments, so smartphone VIS captures could be checked against legacy NIR galleries.
  • Natural next step not in the paper: because the app already records color and reflection information in the VIS channel, the same quality-checked frames could support presentation-attack detection without additional capture hardware.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 6 minor

Summary. The paper describes a smartphone-based iris recognition system for visible-spectrum capture. It introduces an Android application that uses YOLOv3-tiny for eye/iris detection, automated zoom and focus adjustments, ISO/IEC 29794-6 quality checks, and a lightweight Ghost-Attention U-Net (G-ATTU-Net) for iris segmentation. The authors introduce the CUVIRIS dataset of 47 subjects with 940 VIS and NIR images, and report verification results in terms of TAR at FAR 0.01% for VIS, NIR, cross-spectral, distance-based, and iris-color-based comparisons. The central claim is that high-quality smartphone-captured VIS iris images can achieve recognition accuracy comparable to NIR, and that the proposed app and segmentation model make this practical on mobile devices.

Significance. If the reported performance held out-of-sample, this would be a useful contribution to mobile iris recognition: the CUVIRIS dataset is a novel public resource with modern smartphone captures and ISO-based quality checks, and the G-ATTU-Net model is a plausible lightweight alternative to heavier segmentation networks. The cross-spectral NIR-to-VIS comparison also addresses an application-relevant scenario. However, the current evaluation does not yet support the generalization claims in the abstract. The main strengths are the dataset, the real-time mobile capture pipeline, and the clear description of the app's control loop; the main weakness is that the accuracy numbers are computed in-sample, with the segmentation model fine-tuned on the same data used for evaluation and with the operating threshold set on the same small cohort. These issues are structural and require additional experiments or analysis rather than simple rewording.

major comments (4)
  1. [Section III.B.1 and Section VIII] The G-ATTU-Net segmentation model is fine-tuned on the CUVIRIS dataset (Section III.B.1) and then used to produce masks for the same CUVIRIS images in the recognition evaluation of Section VIII. No subject-disjoint or image-disjoint split is described for this fine-tuning. Because the same subjects, and very likely the same images, appear in both fine-tuning and evaluation, the reported TAR values estimate the performance of a segmentation module that has already seen the test irises. This can inflate accuracy if the model has overfit to subject-specific appearance or to the specific acquisition conditions of this single session. The authors should either use a strict subject-disjoint split for segmentation training versus recognition evaluation, or show that the fine-tuning has no effect on the masks used in the recognition experiment.
  2. [Section VIII] The paper reports TAR at FAR 0.01% but does not describe how the Hamming-distance decision threshold is selected. If the threshold is derived from the impostor score distribution of the same 47-subject evaluation set, as is typical for ROC-based reporting, the operating point is chosen in-sample. With 47 subjects the impostor comparison pool is small (on the order of 10^4 comparisons per eye), so the 99.99th percentile of impostor scores is imprecisely estimated; one or two additional impostor scores can shift the threshold materially. The authors should report confidence intervals for the TAR at the chosen FAR, and either fix the threshold using a held-out set or describe an a priori threshold-setting protocol.
  3. [Section IV and Section VIII] The contribution of the capture application is not isolated. The paper motivates the dedicated app through automatic zoom, focus, and quality control, but the recognition experiments in Section VIII compare only images captured by the full app against NIR images and against previously published datasets. There is no comparison with images captured by the same phone's stock camera application, or with the app's auto-zoom or quality checks disabled, using the same recognition pipeline. Without this baseline, it is not possible to determine whether the reported TAR is attributable to the app's capture features, to the phone's sensor, or to the processing pipeline. This is load-bearing for the paper's central claim that the application enables high-quality VIS iris capture.
  4. [Section VI and Section VIII] All TAR figures are based on 47 subjects and a single session per subject, with no confidence intervals or subject-level bootstrap. For example, the VIS left-eye genuine comparison uses roughly 329 comparisons (47 subjects x 7 verification images), so a TAR of 96.57% corresponds to about 12 false rejects; the binomial uncertainty is several percentage points. The abstract's precise numbers therefore overstate the certainty of the estimates. At minimum, the authors should report confidence intervals and, where possible, subject-level bootstrap resampling.
minor comments (6)
  1. [Section II, Section III.A] The UBIRIS-V1 dataset is cited as [11], but reference [11] in the bibliography is Edwards et al. on iris color; the correct Proença et al. reference for UBIRIS-V1 appears to be missing or misnumbered.
  2. [Section I] The text contains an unresolved placeholder 'see Figure ??' in the bullet list of application features; all figure references should be resolved before submission.
  3. [Section IV.A.3 and Table II] The Iris-Pupil Ratio metric is listed with a range of 0-100 and threshold >20 in Table II, while the text states its actual range is 9.58 to 121.30; these values should be reconciled.
  4. [Section IV.A.4 and Section IV.A.5] These subsections duplicate, almost verbatim, the YOLOv3-tiny and G-ATTU-Net descriptions already given in Section III.A and Section III.B. Please consolidate to avoid repetition.
  5. [Section VIII] The impostor comparison protocol is ambiguous: 'a randomly selected enrollment template from one subject was compared against all other subjects' images' does not specify whether this is done once per eye or repeatedly, and whether the random selection affects the reported TAR. Please clarify.
  6. [Section VIII and Figure 12] The text calls the iris-color comparisons 'ROC curves' while the figure caption says 'DET plots'; please use consistent terminology.

Circularity Check

1 steps flagged · score 6.0 of 10

Reported TAR is an in-sample estimate: G-ATTU-Net was fine-tuned on the same CUVIRIS images later used for verification, so the headline accuracy is not an independent prediction.

  1. fitted input called prediction [Section III.B.1 (G-ATTU-Net Training and Evaluation) and Section VIII (Experiments on CUVIRIS Dataset)]
    ""After the initial training phase, both models were fine-tuned on the CUVIRIS dataset." "To evaluate the performance of the developed application and segmentation techniques, we conducted verification and identification experiments using the CUVIRIS dataset.""

    The masks consumed by the Hamming-distance matcher (Eq. 1) come from G-ATTU-Net, whose weights were fine-tuned on CUVIRIS (Section III.B.1). The verification experiments in Section VIII then compute all headline TAR values on that same CUVIRIS set, with the first image per subject used as enrollment and the remainder as verification. Thus the reported 96.57%/97.95%/96.17-96.31% TAR values are training-set measurements of a segmentation model that has already seen the test subjects, not out-of-sample predictions of system performance.

full rationale

The central experimental claim is the TAR validation. Since the segmentation model is fine-tuned on CUVIRIS and the recognition evaluation is on CUVIRIS, the mask input to every HD calculation is produced by a model fitted to the same subjects; this is a fitted-input/prediction loop. The paper's other contributions — the Android capture app, automatic zoom/focus, ISO quality checks, and the lightweight G-ATTU-Net architecture — are not themselves circular and are compared to external datasets for segmentation training (CSIP, MICHE, UBIRIS, etc.). However, the headline recognition result, which is the abstract's main validation claim, lacks an out-of-sample basis. The discussion (Section IX) acknowledges the small controlled dataset, but the abstract's definitive TAR numbers do not carry this caveat. No self-citation chain or uniqueness import is involved. Score reflects partial circularity in the central validation claim, not an allegation of deliberate bias.

Assumptions & free parameters 4 free parameters · 4 assumptions · 0 invented entities

The paper introduces no new physical entities. Its free parameters are engineering choices (quality thresholds, zoom target, matching threshold) and its axioms are domain assumptions about the suitability of ISO quality metrics, OSIRIS codes, CHT, and a 47-subject dataset for evaluating the claimed recognition performance.

free parameters (4)
  • ISO/IEC 29794-6 quality thresholds = e.g., Overall Quality >70, Sharpness >80, Iris-Pupil Contrast >30
    Hand-chosen thresholds in Table II filter which captured frames are kept; they shape the dataset and therefore the reported TAR.
  • Hamming distance decision threshold = not reported (set to achieve FAR=0.01%)
    The matching threshold is selected on the same CUVIRIS data used for evaluation, making TAR an in-sample operating-point measure.
  • Target bounding box width for auto-zoom = not specified
    The zoom factor in Section IV.A.6 depends on a hand-set target width; it determines framing and thus usable iris area.
  • Rotational shift range (±7 bits) = 7
    Used for alignment before matching; a fixed protocol choice that affects match scores.
assumptions (4)
  • domain assumption ISO/IEC 29794-6 iris quality metrics and the chosen thresholds define what counts as a 'high-quality' VIS iris image.
    Invoked in Section IV.A.3 and Table II; if these metrics are not predictive of recognition performance for visible-light smartphone images, the quality-gating claim is unsupported.
  • domain assumption OSIRIS v4.1 Gabor-based iris codes, designed for NIR images, are a valid representation for matching visible-light iris images and cross-spectrally against NIR codes.
    The matching pipeline in Section VII uses OSIRIS on normalized VIS images with no cross-spectral compensation; the paper provides only empirical support on 47 subjects.
  • domain assumption The Circular Hough Transform reliably estimates pupillary and limbic boundaries in VIS images after segmentation.
    Section VII.A applies CHT for normalization; CHT is known to fail on non-circular, occluded, or low-contrast visible-light boundaries, so recognition accuracy depends on this assumption.
  • domain assumption 47 subjects are representative of a diverse population for the purpose of estimating recognition accuracy.
    Section VI describes distribution of race, age, gender, and iris color, but the small sample size cannot support reliable TAR estimates at 0.01% FAR.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Smartphone-based Iris Recognition through High-Quality Visible Spectrum Iris Capture." pith.science (2026). https://pith.science/paper/GKO77XPI

@misc{pith2026241213063,
  author       = {Pith},
  title        = {Pith review of: Smartphone-based Iris Recognition through High-Quality Visible Spectrum Iris Capture},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/GKO77XPI}},
  note         = {Machine review of arXiv:2412.13063}
}
read the original abstract

Iris recognition is widely acknowledged for its exceptional accuracy in biometric authentication, traditionally relying on near-infrared (NIR) imaging. Recently, visible spectrum (VIS) imaging via accessible smartphone cameras has been explored for biometric capture. However, a thorough study of iris recognition using smartphone-captured 'High-Quality' VIS images and cross-spectral matching with previously enrolled NIR images has not been conducted. The primary challenge lies in capturing high-quality biometrics, a known limitation of smartphone cameras. This study introduces a novel Android application designed to consistently capture high-quality VIS iris images through automated focus and zoom adjustments. The application integrates a YOLOv3-tiny model for precise eye and iris detection and a lightweight Ghost-Attention U-Net (G-ATTU-Net) for segmentation, while adhering to ISO/IEC 29794-6 standards for image quality. The approach was validated using smartphone-captured VIS and NIR iris images from 47 subjects, achieving a True Acceptance Rate (TAR) of 96.57% for VIS images and 97.95% for NIR images, with consistent performance across various capture distances and iris colors. This robust solution is expected to significantly advance the field of iris biometrics, with important implications for enhancing smartphone security.

Figures

Figures reproduced from arXiv: 2412.13063 by the authors.

Figure 1
Figure 1. Automatic Focal Point Adjustment. The left column shows out-of￾focus eye images before the focal point adjustment, while the right column shows the corresponding images after the focus is automatically adjusted to the iris region. els employed in our proposed system, including the YOLOv3- tiny and G-ATTU-Net architectures. Section IV presents the experimental setup, data collection methodology, and the re￾sults obta… view at source ↗
Figure 2
Figure 2. The architecture of the proposed G-ATTU-Net model. Each dark blue block represents a multi-channel feature map produced by a Ghost Module, with the numbers above indicating the number of channels and the vertical numbers on the left indicating the spatial dimensions (height x width). The red blocks indicate Attention Blocks used to refine features before concatenation in the decoder path. Net architecture by incorpo… view at source ↗
Figure 3
Figure 3. The input images and segmentation results using both the standard Attention U-Net and the GAtt-UNet. The first column shows the red channel images extracted from RGB inputs after cropping the eye region using the YOLOv3-Tiny eye and iris detection model from the CUVIRIS dataset. The second column contains the ground truth masks for the iris region. The third column displays the segmentation results from the standard… view at source ↗
Figures from the paper (8 more)
Figure 4
Figure 4. Figure 4: (a) User interface for inputting participant details and (b) camera preview displaying detected eye and iris bounding boxes with real-time user feedback, ensuring accurate and focused image capture. YOLOV3 eye and iris detection model (Section III.A) was initialized, a…
Figure 5
Figure 5. Figure 5: Process flow of the Android application for capturing and processing iris images. distance from the camera. Meanwhile, the iris coordinates, after being scaled back to the original image size, are used to fine-tune the camera’s focal length. The autofocus system dynami…
Figure 7
Figure 7. Figure 7: Race, age, gender and iris color distribution in CUVIRIS the database. application include a total of 752 images, with 8 images per eye—4 captured at a distance of 25cm and another 4 at a distance of 50cm. Each participant contributed a total of 20 images: 4 NIR images…
Figure 8
Figure 8. Figure 8: Sample cropped irides images from the CUVIRIS dataset [PITH_FULL_IMAGE:figures/full_fig_p011_8.png]
Figure 9
Figure 9. Figure 9: TNormalization and code generation. The left column shows normalized iris images after rubbersheet transformation, the middle column displays the corresponding normalized masks, and the right column presents the iris codes generated by the OSIRIS v4.1 toolkit [PITH_FU…
Figure 10
Figure 10. Figure 10: ROC curves for various comparisons: (a) visible light, (b) NIR, and (c) cross-spectral [PITH_FULL_IMAGE:figures/full_fig_p011_10.png]
Figure 11
Figure 11. Figure 11: ROC curve for distance-based comparison. VIII. EXPERIMENTS ON CUVIRIS DATASET To evaluate the performance of the developed application and segmentation techniques, we conducted verification and identification experiments using the CUVIRIS dataset. Perfor￾mance was ass…
Figure 12
Figure 12. Figure 12: ROC curves for color-based comparison: (a) blue, (b) brown, and (c) gray irises. achieving a TAR of 96.57% for the left iris and 96.81% for the right iris. Subsequently, the NIR images were compared, yielding a TAR of 97.95% for both the left and right irises. We then…

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

25 extracted references · 25 canonical work pages

  1. [1]

    This pin can be easily guessed: Analyzing the security of smartphone unlock pins,

    P. Markert, D. V . Bailey, M. Golla, M. D ¨urmuth, and A. J. Aviv, “This pin can be easily guessed: Analyzing the security of smartphone unlock pins,” in 2020 IEEE Symposium on Security and Privacy (SP) . IEEE, 2020, pp. 286–303

    work page 2020

  2. [2]

    Biometric authentication methods on smartphones: A survey

    R. Spolaor, Q. Li, M. Monaro, M. Conti, L. Gamberini, and G. Sartori, “Biometric authentication methods on smartphones: A survey.” Psych- Nology Journal, no. 2, 2016

    work page 2016

  3. [3]

    Image understanding for iris biometrics: A survey,

    K. W. Bowyer, K. Hollingsworth, and P. J. Flynn, “Image understanding for iris biometrics: A survey,” Computer vision and image understand- ing, vol. 110, no. 2, pp. 281–307, 2008

    work page 2008

  4. [4]

    How iris recognition works,

    J. Daugman, “How iris recognition works,” in The essential guide to image processing. Elsevier, 2009, pp. 715–739

    work page 2009

  5. [5]

    Smartphone based visible iris recognition using deep sparse filtering,

    K. B. Raja, R. Raghavendra, V . K. Vemuri, and C. Busch, “Smartphone based visible iris recognition using deep sparse filtering,” Pattern Recog- nition Letters , vol. 57, pp. 33–42, 2015

    work page 2015

  6. [6]

    Pigment melanin: Pattern for iris recognition,

    M. S. Hosseini, B. N. Araabi, and H. Soltanian-Zadeh, “Pigment melanin: Pattern for iris recognition,” IEEE transactions on instrumen- tation and measurement , vol. 59, no. 4, pp. 792–804, 2010

    work page 2010

  7. [7]

    A comprehensive review on iris image- based biometric system,

    J. J. Winston and D. J. Hemanth, “A comprehensive review on iris image- based biometric system,” Soft Computing, vol. 23, pp. 9361–9384, 2019

    work page 2019

  8. [8]

    Icip 2016 competition on mobile ocular biometric recognition,

    A. Rattani, R. Derakhshani, S. K. Saripalle, and V . Gottemukkula, “Icip 2016 competition on mobile ocular biometric recognition,” in 2016 IEEE international conference on image processing (ICIP) . IEEE, 2016, pp. 320–324

    work page 2016

Show all 25 references
  1. [9]

    Mobile iris challenge evaluation (miche)-i, biometric iris dataset and protocols,

    M. De Marsico, M. Nappi, D. Riccio, and H. Wechsler, “Mobile iris challenge evaluation (miche)-i, biometric iris dataset and protocols,” Pattern Recognition Letters , vol. 57, pp. 17–23, 2015

    2015

  2. [10]

    Fusing iris and periocular information for cross-sensor recognition,

    G. Santos, E. Grancho, M. V . Bernardo, and P. T. Fiadeiro, “Fusing iris and periocular information for cross-sensor recognition,” Pattern Recognition Letters , vol. 57, pp. 52–59, 2015

    2015

  3. [11]

    Quantita- tive measures of iris color using high resolution photographs,

    M. Edwards, A. Gozdzik, K. Ross, J. Miles, and E. J. Parra, “Quantita- tive measures of iris color using high resolution photographs,” American journal of physical anthropology , vol. 147, no. 1, pp. 141–149, 2012

    2012

  4. [12]

    The ubiris. v2: A database of visible wavelength iris images captured on- the-move and at-a-distance,

    H. Proenc ¸a, S. Filipe, R. Santos, J. Oliveira, and L. A. Alexandre, “The ubiris. v2: A database of visible wavelength iris images captured on- the-move and at-a-distance,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 32, no. 8, pp. 1529–1535, 2009

    2009

  5. [13]

    ISO/IEC 29794-6:2015 Information Technology - Biometric Sample Quality -Part 6: Iris Image Data,

    “ISO/IEC 29794-6:2015 Information Technology - Biometric Sample Quality -Part 6: Iris Image Data,” ISO(International Organization for Standardization) and IEC (International Electrotechnical Commission

    2015

  6. [14]

    IrisGuard,

    “IrisGuard,” https://www.irisguard.com, accessed: 2019-11-147

    2019

  7. [15]

    Iris imaging in visible spectrum using white led,

    K. B. Raja, R. Raghavendra, and C. Busch, “Iris imaging in visible spectrum using white led,” in 2015 IEEE 7th International Conference on Biometrics Theory, Applications and Systems (BTAS) . IEEE, 2015, pp. 1–8

    2015

  8. [16]

    Iris recognition: On the segmentation of degraded images acquired in the visible wavelength,

    H. Proenca, “Iris recognition: On the segmentation of degraded images acquired in the visible wavelength,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 32, no. 8, pp. 1502–1516, 2009

    2009

  9. [17]

    Iris recognition with a database of iris images obtained in visible light using smartphone camera,

    M. Trokielewicz, “Iris recognition with a database of iris images obtained in visible light using smartphone camera,” in 2016 IEEE International Conference on Identity, Security and Behavior Analysis (ISBA). IEEE, 2016, pp. 1–6

    2016

  10. [18]

    Yolo v3-tiny: Object detection and recognition using one stage improved model,

    P. Adarsh, P. Rathi, and M. Kumar, “Yolo v3-tiny: Object detection and recognition using one stage improved model,” in 2020 6th interna- tional conference on advanced computing and communication systems (ICACCS). IEEE, 2020, pp. 687–694

    2020

  11. [19]

    An overview of the hdf5 technology suite and its applications,

    M. Folk, G. Heber, Q. Koziol, E. Pourmal, and D. Robinson, “An overview of the hdf5 technology suite and its applications,” in Pro- ceedings of the EDBT/ICDT 2011 workshop on array databases , 2011, pp. 36–47

    2011

  12. [20]

    Deep learning-based iris segmentation for iris recognition in visible light environment,

    M. Arsalan, H. G. Hong, R. A. Naqvi, M. B. Lee, M. C. Kim, D. S. Kim, C. S. Kim, and K. R. Park, “Deep learning-based iris segmentation for iris recognition in visible light environment,” Symmetry, vol. 9, no. 11, p. 263, 2017

    2017

  13. [21]

    Iris segmentation for non-ideal iris biometric systems,

    F. Jan, S. Alrashed, and N. Min-Allah, “Iris segmentation for non-ideal iris biometric systems,” Multimedia Tools and Applications , vol. 83, no. 5, pp. 15 223–15 251, 2024

    2024

  14. [22]

    A robust iris segmentation scheme based on improved u-net,

    W. Zhang, X. Lu, Y . Gu, Y . Liu, X. Meng, and J. Li, “A robust iris segmentation scheme based on improved u-net,” IEEE access , vol. 7, pp. 85 082–85 089, 2019

    2019

  15. [23]

    Attention guided u-net for accurate iris segmentation,

    S. Lian, Z. Luo, Z. Zhong, X. Lin, S. Su, and S. Li, “Attention guided u-net for accurate iris segmentation,” Journal of Visual Communication and Image Representation , vol. 56, pp. 296–304, 2018

    2018

  16. [24]

    Exploiting superior cnn-based iris segmentation for better recognition accuracy,

    H. Hofbauer, E. Jalilian, and A. Uhl, “Exploiting superior cnn-based iris segmentation for better recognition accuracy,” Pattern Recognition Letters, vol. 120, pp. 17–23, 2019

    2019

  17. [25]

    Osiris: An open source iris recognition software,

    N. Othman, B. Dorizzi, and S. Garcia-Salicetti, “Osiris: An open source iris recognition software,” Pattern recognition letters , vol. 82, pp. 124– 131, 2016

    2016

Pith tools

Reviewed August 11, 2026 · model on record in the stance chip above.