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REVIEW 5 major objections 4 minor 34 references

OcularAge: A Comparative Study of Iris and Periocular Images for Pediatric Age Estimation

T0 review · 5 major / 4 minor · reviewed 2026-08-15 · deepseek-v4-flash

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Pith's one-line read A child's age is readable from near-infrared eye images with a mean error of 1.33 years, and periocular context beats the iris alone.

desk verdict Useful first pediatric ocular age-estimation benchmark, but the headline periocular-vs-iris comparison is confounded by input resolution and normalization, and the 'reliable' claim outruns the evidence. read the letter →

arxiv 2505.05374 v1 pith:VFE4RI3F submitted 2025-05-08 eess.IV cs.CV

Naveenkumar G Venkataswamy , Poorna Ravi , Stephanie Schuckers , Masudul H. Imtiaz This is my paper · ORCID
classification eess.IVcs.CV
keywords pediatricageestimationirisbiometricsperiocularrecognitiondeeplearningmulti-tasknear-infraredimagingsensorbiasvirtualreality
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 asks whether a child's age can be estimated from an eye image and which portion of the eye carries the most age-related signal. Using a longitudinal set of more than 21,000 near-infrared ocular images from 288 children aged 4 to 16, collected over eight years with two different sensors, the authors train multi-task convolutional networks to jointly predict an age group (4–9 versus 10–16 years) and an exact age. They report that models trained on whole periocular eye images consistently outperform models trained on the normalized iris alone: the best model reaches a mean absolute error of 1.33 years and 83.82% binary age-group accuracy. The paper presents this as the first demonstration that reliable pediatric age estimation from ocular images is feasible, with practical value for privacy-preserving age checks in child-centric applications.

What carries the argument

The engine of the comparison is a multi-task deep-learning setup in which each CNN has two output heads: a binary classifier for age group and a regressor for exact age, trained jointly with focal loss for classification and mean squared error for regression. The two input modalities are matched pairs: a grayscale full-eye image at 320×240 pixels and the iris region segmented and normalized to a 256×32 strip (using an open-source iris segmentation toolkit) with a binary occlusion mask concatenated as a second channel. Subject-exclusive splits, identical training configurations, and the same six backbones across both modalities make the eye-versus-iris difference attributable to input content rather than to architecture or data leakage.

What would settle it

Compute the same metrics using exact ages from birth dates and capture dates on this longitudinal dataset, or on a similarly sized pediatric ocular dataset. If the mean absolute error with exact labels is markedly larger than the year-only MAE (for example, above 2 years), then the reported accuracy largely reflects label quantization rather than true age estimation.

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Extended reading notes

Core claim

The central claim is that periocular context—eyelids, skin folds, sclera, and lash lines—carries more age-discriminative information for children than the iris texture pattern itself, and this holds across every architecture tested. MobileNetV3-Large on grayscale eye images achieves a MAE of 1.33 years and 83.82% age-group accuracy, versus 2.32 years and 71.00% for the same model on normalized iris strips. The paper further claims to establish the first systematic longitudinal benchmark comparing iris and periocular input for pediatric age estimation, showing that the best models generalize to an unseen sensor (EfficientNet-B3: 85.86% accuracy, 1.37 MAE in cross-sensor evaluation) and that a quantized MobileNetV3 runs in about 10 ms per image on a standalone VR headset, meeting real-time constraints.

Load-bearing premise

Ground-truth ages are computed as birth year minus capture year, producing integer labels that can be off by up to nearly a year, and the reported errors are measured against this coarse clock.

Editorial extensions

If this is right

  • Eye-based models outperform iris-based models for every architecture tested, so periocular imagery is the better input modality for pediatric ocular age estimation.
  • The best eye-based models remain stable across developmental age bins, with the hardest groups at the extremes (ages 4–6 and 13–16) and the most predictable range in mid-childhood.
  • Model confidence drops around ages 8–10, near the group boundary, suggesting that practical systems should treat this transition zone with uncertainty-aware logic and escalate to human review when confidence is low.
  • Cross-sensor experiments show that models trained on one NIR camera transfer to a second unseen camera with only modest degradation for MobileNetV3 and EfficientNet-B3, supporting deployment in heterogeneous hardware environments.
  • A lightweight model runs in under 10 ms per image on a standalone VR headset, indicating that on-device, privacy-preserving age checks are technically plausible in real-time child-centric applications.

Reading between the lines

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

  • The reported 1.33-year MAE is measured against year-granularity labels; with month-exact birth dates the true error could be somewhat larger, and re-benchmarking with exact ages would give a fairer headline number.
  • The same framework could likely be transferred to RGB images from the eye-tracking cameras already built into many VR headsets, removing the need for an added near-infrared module.
  • Fusing iris and periocular streams, or replacing the hard 10-year boundary with a soft or continuous transition, may push accuracy higher, especially in the 8–10 age range where confidence currently dips.
  • The moderate iris-only performance hints that even a supposedly stable biometric trait can encode auxiliary age signals; higher-resolution longitudinal iris capture could test whether that signal is genuine developmental biology or an artifact of normalization.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

5 major / 4 minor

Summary. This manuscript presents OcularAge, an empirical comparison of iris versus periocular (eye) images for estimating the age of pediatric subjects (4–16 years) using a longitudinal NIR dataset of 21,922 eye images from 288 children collected over eight years with two sensors. The authors train six CNN backbones (EfficientNet-B3, MobileNetV3, ResNet-50, DenseNet-121, ConvNeXt-Tiny, MobileViT-S) in a multi-task framework with age regression and binary age-group classification, using subject-exclusive splits. They report best periocular performance of MAE 1.33 years and 83.82% accuracy (MobileNetV3), outperforming iris-based models; additional experiments examine age-group-wise error, model confidence trends, cross-sensor generalization (IG-AD100 training to iCAM T10 testing), and deployment latency on GPU, Jetson, and Oculus Quest 2.

Significance. The work addresses an under-explored problem and provides a useful benchmark with subject-exclusive evaluation and realistic deployment profiling. The strongest contributions are the longitudinal dataset utilization, the multi-architecture comparison, and the end-to-end latency measurements on embedded/VR hardware. The comparative claim that periocular images are more informative than normalized iris images is, however, not yet established because the two input representations differ not only in anatomical region but also in resolution and geometric normalization. The absolute performance numbers are also affected by coarse integer age labels and single-run results. With these caveats addressed, the benchmark would be a valuable reference for child-focused biometric age estimation.

major comments (5)
  1. [Section III-A2, IV-C, Table 7] The iris versus eye comparison is confounded by input representation. Eye inputs are 320x240 grayscale images (76,800 pixels) with unnormalized geometry, while iris inputs are Daugman-normalized 256x32 strips (8,192 pixels) plus a binary mask. The observed gap (e.g., MobileNetV3: 83.82% vs 71.00%, MAE 1.33 vs 2.32) may reflect pixel budget and normalization rather than anatomical informativeness. The attribution to 'richer spatial and contextual information' in Section IV-C is a restatement of the confound. To support the central comparative claim, the authors should add a control experiment that holds pixel budget and geometric representation fixed, such as resizing the normalized iris strip to the same resolution as the eye image or cropping the eye image to the iris region without rubber-sheet normalization.
  2. [Section III-A2 and Discussion] The ground-truth ages are computed as capture year minus birth year, yielding integer labels with up to one year of error. The reported MAE of 1.33 years is therefore not a direct measure of error against true chronological age. The authors acknowledge the granularity issue in the Discussion ('age labels were based on birth year metadata, which introduces some granularity issues') but do not bound or correct for it. A quantitative analysis, such as reporting expected error under a uniform within-year distribution or using month-level metadata where available, is needed before the absolute MAE claim can be taken at face value.
  3. [Section III-C6 and Tables 4–10] All results are single-run point estimates from one random seed. Several conclusions rest on small differences between models (e.g., MobileNetV3 and EfficientNet-B3 both report MAE 1.33 in Table 7). Without multiple seeds or bootstrap confidence intervals, the model rankings and the 'consistent outperformance' claim are not statistically supported. Please report means and standard deviations over at least three seeds, or provide equivalent uncertainty estimates, for the main result tables.
  4. [Section III-A1 and Table 1] The reported image counts are internally inconsistent. The text states that the IG-AD100 contributed 16,285 images and the iCAM T10 5,014 images (Section III-A1); a later sentence says 16,885 eye images with 5,337 iCAM images; and Table 1 lists 16,585 IG-AD100 and 5,337 iCAM eye images. After preprocessing, the iris counts are 16,422 and 5,197. These discrepancies make the dataset description unreliable and must be reconciled before reproducibility can be assessed.
  5. [Section IV-A, Table 4] ConvNeXt-Tiny achieves 45.23% binary accuracy with F1=0.00 and 0% ±1-year accuracy, which is at or below chance and indicates a training failure rather than genuine underperformance. This suggests a possible bug in the architecture adaptation (e.g., input stem modification) or optimization schedule. The authors should diagnose this failure or exclude the model from the benchmark; as reported, it distorts the claim of benchmarking six architectures.
minor comments (4)
  1. [Section IV-G] The deployment table is referred to as 'Table ??' in the text; this should read 'Table 11.'
  2. [Section III-A2] The sentence 'No explicit segmentation or masking was applied to periocular images' is followed later by the description of normalization statistics computed from the training set; consider clarifying that no anatomical masks are used for eye inputs, to avoid ambiguity about masking versus intensity normalization.
  3. [Section IV-F, Tables 9–10] The cross-sensor results show EfficientNet-B3 achieving higher accuracy on the unseen sensor (85.86%) than on the same sensor (79.98% in Table 9). This non-monotonic behavior deserves an explanation, as it may reflect different test-set compositions rather than true sensor-agnostic performance.
  4. [Section V] The claim of 'first demonstration that reliable age estimation is feasible from children's ocular images' should be scoped more carefully, since prior iris-age studies on broader populations are cited; the uniqueness claim should be compared against those works in the discussion.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the central modality comparison and age-estimation results come from held-out evaluations, not from fitted inputs or self-citation chains.

full rationale

The paper's load-bearing claims—periocular models outperform iris models (MAE 1.33 vs. 2.32 years; classification accuracy 83.82% vs. 71.00% for MobileNetV3), reliable pediatric age estimation is feasible, and cross-sensor performance degrades gracefully—are all supported by subject-exclusive train/validation/test splits and direct empirical evaluation. No analytic derivation is offered whose output is equivalent to a fitted quantity or to an input label by construction. The age labels are computed by subtracting birth year from capture year, but this is a measurement granularity issue acknowledged by the authors, not a circular redefinition of the predicted quantity. The prior works cited from the same group ([19], [20]) establish dataset provenance and pediatric iris stability; they are not used as a substitute for the present comparisons, and the central claim does not depend on any self-authored equation, uniqueness theorem, or ansatz imported by citation. The reviewer-identified confound—that eye inputs are 320x240 grayscale images while iris inputs are 256x32 normalized strips with a mask—is a real experimental-design limitation regarding whether the comparison isolates anatomical region versus input representation, but it is not an instance of circularity under the stated criteria: the results are not forced by definition, and the periocular-vs-iris comparison is not a fitted parameter renamed as a prediction. Therefore no circular step can be exhibited, and the appropriate score is 0.

Assumptions & free parameters 7 free parameters · 6 assumptions · 0 invented entities

The paper contains no analytic derivation; the central claim is an empirical benchmark. The fitted quantities are the trained network weights plus hand-chosen hyperparameters and normalization statistics, listed above. The assumptions are domain assumptions about label quality, transfer learning, segmentation correctness, population representativeness, and NIR availability in deployment, all stated or implicit in the methods and discussion.

free parameters (7)
  • Multi-task loss weight alpha = 0.25 (initial), dynamically adjusted
    Chosen by hand in Section III-C3 to balance focal classification and MSE regression losses; all accuracy and MAE numbers depend on this weighting.
  • Age-group boundary = 9/10 years
    The binary classes are defined as ages 4-9 versus 10-16 in Section III-A2; the 83.82% classification accuracy is relative to this hand-chosen split.
  • Normalization statistics for eye images = mean 0.5187, std 0.2505
    Computed from the training set and applied to all eye inputs (Table 3); changes with split would change results.
  • Normalization statistics for iris images = mean 0.2103, std 0.0879
    Computed from the training set for normalized iris inputs (Table 3).
  • Training hyperparameters = batch 64, LR 0.001, weight decay 0.01, patience 5
    Global settings in Table 3; selected by hand and applied to all models, so they shape every reported number.
  • Label smoothing amount = not reported
    Label smoothing is listed as applied in Section III-C5, but the smoothing parameter is not given, leaving an unreported hand choice.
  • Neural network weights (all models) = trained on training split
    The central result is produced by fitting millions of weights to the training images; these are fitted quantities, though too numerous to enumerate individually.
assumptions (6)
  • domain assumption ImageNet-pretrained weights transfer to NIR grayscale ocular images after input-stem modification.
    All models are initialized from ImageNet weights with averaged RGB channels and modified first conv layer (Section III-C2); transferability is assumed, not validated.
  • domain assumption OSIRIS segmentation and normalization produce correct iris boundaries and masks.
    OSIRIS is used to segment and normalize iris regions (Section III-A2); segmentation failures exclude about 300 images, and incorrect segmentation would corrupt iris inputs.
  • domain assumption Age computed from birth year and capture year is valid ground truth.
    Ages are integer calendar-year differences (Section III-A2), acknowledged as coarse in the Discussion; MAE and accuracy are measured against this label.
  • domain assumption The Potsdam, NY longitudinal sample represents the pediatric population.
    288 subjects from local public schools with no demographic diversity analysis; generalization to other populations is assumed in claims of real-world feasibility.
  • domain assumption NIR ocular images will be available in deployment settings.
    The Discussion states that runtime benchmarks assume NIR images and that consumer VR headsets only have RGB cameras; the deployment claim relies on adding NIR hardware.
  • domain assumption Standard deep learning training procedures are adequate for all six architectures.
    The same hyperparameters are used for all models; ConvNeXt-Tiny collapsed to majority class (45.23% accuracy, F1 0.00), suggesting this assumption failed for that architecture.

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Cite this review

Pith. "Pith review of OcularAge: A Comparative Study of Iris and Periocular Images for Pediatric Age Estimation." pith.science (2026). https://pith.science/paper/VFE4RI3F

@misc{pith2026250505374,
  author       = {Pith},
  title        = {Pith review of: OcularAge: A Comparative Study of Iris and Periocular Images for Pediatric Age Estimation},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/VFE4RI3F}},
  note         = {Machine review of arXiv:2505.05374}
}
read the original abstract

Estimating a child's age from ocular biometric images is challenging due to subtle physiological changes and the limited availability of longitudinal datasets. Although most biometric age estimation studies have focused on facial features and adult subjects, pediatric-specific analysis, particularly of the iris and periocular regions, remains relatively unexplored. This study presents a comparative evaluation of iris and periocular images for estimating the ages of children aged between 4 and 16 years. We utilized a longitudinal dataset comprising more than 21,000 near-infrared (NIR) images, collected from 288 pediatric subjects over eight years using two different imaging sensors. A multi-task deep learning framework was employed to jointly perform age prediction and age-group classification, enabling a systematic exploration of how different convolutional neural network (CNN) architectures, particularly those adapted for non-square ocular inputs, capture the complex variability inherent in pediatric eye images. The results show that periocular models consistently outperform iris-based models, achieving a mean absolute error (MAE) of 1.33 years and an age-group classification accuracy of 83.82%. These results mark the first demonstration that reliable age estimation is feasible from children's ocular images, enabling privacy-preserving age checks in child-centric applications. This work establishes the first longitudinal benchmark for pediatric ocular age estimation, providing a foundation for designing robust, child-focused biometric systems. The developed models proved resilient across different imaging sensors, confirming their potential for real-world deployment. They also achieved inference speeds of less than 10 milliseconds per image on resource-constrained VR headsets, demonstrating their suitability for real-time applications.

Figures

Figures reproduced from arXiv: 2505.05374 by the authors.

Figure 1
Figure 1. FIGURE 1: Example NIR eye images captured using two [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 2
Figure 2. FIGURE 2: Distribution of images by subject age. [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. FIGURE 3: Example of the iris pre-processing pipeline. From top to bottom: the original eye image, the corresponding [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figures from the paper (6 more)
Figure 4
Figure 4. Figure 4: FIGURE 4: Overview of the model architecture. Eye-based models take a 1-channel grayscale input, while iris-based models [PITH_FULL_IMAGE:figures/full_fig_p007_4.png]
Figure 5
Figure 5. Figure 5: FIGURE 5: Grad-CAM visualizations from the eye-based MobileNetV3 model. Each image is annotated with the ground truth [PITH_FULL_IMAGE:figures/full_fig_p010_5.png]
Figure 6
Figure 6. Figure 6: FIGURE 6: Grad-CAM visualizations from the iris-based MobileNetV3 model using normalized iris images. The left column [PITH_FULL_IMAGE:figures/full_fig_p011_6.png]
Figure 7
Figure 7. Figure 7: FIGURE 7: MAE across age bins for each eye-based model. MobileNetV3 and EfficientNet-B3 show stable performance, while [PITH_FULL_IMAGE:figures/full_fig_p012_7.png]
Figure 8
Figure 8. Figure 8: FIGURE 8 [PITH_FULL_IMAGE:figures/full_fig_p014_8.png]
Figure 9
Figure 9. Figure 9: FIGURE 9: Average model confidence vs. true age using eye inputs. A consistent dip is observed around the 8–10-year [PITH_FULL_IMAGE:figures/full_fig_p015_9.png]

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Works this paper leans on

34 extracted references · 31 canonical work pages

  1. [1]

    Age estimation with smartphone: Is it reliable for forensics identification? estimasi usia dengan smartphone: Apakah dapat diandalkan dalam identifkasi foren- sik?

    M. S. M. A. Ruth, L. Gita, A. Kurniawan, H. Utomo et al., “Age estimation with smartphone: Is it reliable for forensics identification? estimasi usia dengan smartphone: Apakah dapat diandalkan dalam identifkasi foren- sik?” Dentika: Dental Journal, vol. 23, no. 2, pp. 34–38, 2020

    work page 2020

  2. [2]

    Exploring the perspec- tives of social vr-aware non-parent adults and parents on children’s use of social virtual reality,

    C. Fiani, P. Saeghe, M. McGill, and M. Khamis, “Exploring the perspec- tives of social vr-aware non-parent adults and parents on children’s use of social virtual reality,” Proceedings of the ACM on Human-Computer Interaction, vol. 8, no. CSCW1, pp. 1–25, 2024

    work page 2024

  3. [3]

    Age estimation of face images based on cnn and divide-and-rule strategy,

    H. Liao, Y . Yan, W. Dai, and P. Fan, “Age estimation of face images based on cnn and divide-and-rule strategy,” Mathematical Problems in Engineering, vol. 2018, no. 1, p. 1712686, 2018

    work page 2018

  4. [4]

    Longitudinal evaluation of child face recognition and the impact of underlying age,

    S. Singh, K. Bahmani, and S. Schuckers, “Longitudinal evaluation of child face recognition and the impact of underlying age,” in 2024 IEEE International Joint Conference on Biometrics (IJCB). IEEE, 2024, pp. 1–9

    work page 2024

  5. [5]

    Periocular data fusion for age and gender classification,

    C. Bisogni, L. Cascone, and F. Narducci, “Periocular data fusion for age and gender classification,” Journal of Imaging, vol. 8, no. 11, p. 307, 2022

    work page 2022

  6. [6]

    Analysis of physical ageing effects in iris biometrics,

    M. Fairhurst and M. Erbilek, “Analysis of physical ageing effects in iris biometrics,” IET Computer Vision, vol. 5, no. 6, pp. 358–366, 2011

    work page 2011

  7. [7]

    Does iris change over time?

    H. Mehrotra, M. Vatsa, R. Singh, and B. Majhi, “Does iris change over time?” PloS one, vol. 8, no. 11, p. e78333, 2013

    work page 2013

  8. [8]

    Age prediction from iris biometrics,

    M. Erbilek, M. Fairhurst, and M. C. D. C. Abreu, “Age prediction from iris biometrics,” in 5th International Conference on Imaging for Crime Detection and Prevention (ICDP 2013). IET, 2013, pp. 1–07

    work page 2013

Show all 34 references
  1. [9]

    Age prediction from sclera images using deep learning,

    P. Odion, M. Musa, and S. Shuaibu, “Age prediction from sclera images using deep learning,” Journal of the Nigerian Society of Physical Sciences, pp. 787–787, 2022

    2022

  2. [10]

    Ocular biometrics as a function of age, gender, height, weight, and its association with spherical equivalent in children,

    H. Hashemi, R. Pakzad, M. Khabazkhoob, A. Yekta, M. H. Emamian, and A. Fotouhi, “Ocular biometrics as a function of age, gender, height, weight, and its association with spherical equivalent in children,” European Journal of Ophthalmology, vol. 31, no. 2, pp. 688–697, 2021

    2021

  3. [11]

    Association of choroidal thickness with eye growth: a cross- sectional study of individuals between 4 and 23 years,

    M. Zengin, E. Karahan, S. Yilmaz, E. Cinar, I. Tuncer, and C. Kucuk- erdonmez, “Association of choroidal thickness with eye growth: a cross- sectional study of individuals between 4 and 23 years,” Eye, vol. 28, no. 12, pp. 1482–1487, 2014

    2014

  4. [12]

    Longitudinal changes in macular retinal layer thickness in pediatric populations: Myopic vs non- myopic eyes,

    S. A. Read, D. Alonso-Caneiro, and S. J. Vincent, “Longitudinal changes in macular retinal layer thickness in pediatric populations: Myopic vs non- myopic eyes,” PLoS One, vol. 12, no. 6, p. e0180462, 2017

    2017

  5. [13]

    The prediction of old and young subjects from iris texture,

    A. Sgroi, K. W. Bowyer, and P. J. Flynn, “The prediction of old and young subjects from iris texture,” in 2013 International Conference on Biometrics (ICB). IEEE, 2013, pp. 1–5

    2013

  6. [14]

    Facial masks and soft-biometrics: Leveraging face recognition cnns for age and gender prediction on mobile ocular images,

    F. Alonso-Fernandez, K. Hernandez-Diaz, S. Ramis, F. J. Perales, and J. Bigun, “Facial masks and soft-biometrics: Leveraging face recognition cnns for age and gender prediction on mobile ocular images,” IET Biomet- rics, vol. 10, no. 5, pp. 562–580, 2021

    2021

  7. [15]

    Deepirisnet: Deep iris representation with applications in iris recognition and cross-sensor iris recognition,

    A. Gangwar and A. Joshi, “Deepirisnet: Deep iris representation with applications in iris recognition and cross-sensor iris recognition,” in 2016 IEEE international conference on image processing (ICIP). IEEE, 2016, pp. 2301–2305

    2016

  8. [16]

    User acceptance of virtual reality: an extended technology acceptance model,

    C. Sagnier, E. Loup-Escande, D. Lourdeaux, I. Thouvenin, and G. Valléry, “User acceptance of virtual reality: an extended technology acceptance model,” International Journal of Human–Computer Interaction, vol. 36, no. 11, pp. 993–1007, 2020

    2020

  9. [17]

    Accessed: 24 March 2025

    vrchat. Accessed: 24 March 2025. [Online]. Available: https://wiki.vrchat.com/wiki/Age_Verification

    2025

  10. [18]

    Accessed: 24 March 2025

    persona. Accessed: 24 March 2025. [Online]. Available: https://withpersona.com/

    2025

  11. [19]

    Iris recognition performance in children: A longitudinal study,

    P. Das, L. Holsopple, D. Rissacher, M. Schuckers, and S. Schuckers, “Iris recognition performance in children: A longitudinal study,” IEEE Transactions on Biometrics, Behavior, and Identity Science, vol. 3, no. 1, pp. 138–151, 2021

    2021

  12. [20]

    Longitudinal performance of iris recognition in chil- dren: Time intervals up to six years,

    P. Das, N. G. Venkataswamy, L. Holsopple, M. H. Imtiaz, M. Schuckers, and S. Schuckers, “Longitudinal performance of iris recognition in chil- dren: Time intervals up to six years,” in 2023 11th International Workshop on Biometrics and Forensics (IWBF). IEEE, 2023, pp. 1–6

    2023

  13. [21]

    Deep learning based gender and age estimation from human iris,

    M. Rajput and G. Sable, “Deep learning based gender and age estimation from human iris,” in Proceedings of the international conference on ad- vances in electronics, electrical and computational intelligence (ICAEEC), 2019

    2019

  14. [22]

    Five-year change in choroidal thickness in relation to body development and axial eye elongation: the ccc2000 eye study,

    M. H. Hansen, X. Q. Li, M. Larsen, E. M. Olsen, A. M. Skovgaard, L. Kessel, and I. C. Munch, “Five-year change in choroidal thickness in relation to body development and axial eye elongation: the ccc2000 eye study,” Investigative ophthalmology and visual science, vol. 60, no. ...

    2019

  15. [23]

    Differences in the pupillary responses to evening light between children and adolescents,

    L. E. Hartstein, M. K. LeBourgeois, M. T. Durniak, and R. P. Najjar, “Differences in the pupillary responses to evening light between children and adolescents,” Journal of Physiological Anthropology, vol. 43, no. 1, p. 16, 2024

    2024

  16. [24]

    Accessed: 24 March 2025

    meta. Accessed: 24 March 2025. [Online]. Available: https://developers.meta.com/horizon

    2025

  17. [25]

    Classification of remote sensing images using efficientnet-b3 cnn model with attention,

    H. Alhichri, A. S. Alswayed, Y . Bazi, N. Ammour, and N. A. Alajlan, “Classification of remote sensing images using efficientnet-b3 cnn model with attention,” IEEE access, vol. 9, pp. 14 078–14 094, 2021

    2021

  18. [26]

    Mobilenetv3,

    B. Koonce and B. Koonce, “Mobilenetv3,” Convolutional Neural Net- works with Swift for Tensorflow: Image Recognition and Dataset Cate- gorization, pp. 125–144, 2021

    2021

  19. [27]

    Resnet 50,

    B. Koonce, “Resnet 50,” in Convolutional neural networks with swift for tensorflow: image recognition and dataset categorization. Springer, 2021, pp. 63–72

    2021

  20. [28]

    Densely connected convolutional networks,

    G. Huang, Z. Liu, L. Van Der Maaten, and K. Q. Weinberger, “Densely connected convolutional networks,” in Proceedings of the IEEE confer- ence on computer vision and pattern recognition, 2017, pp. 4700–4708

    2017

  21. [29]

    A convnet for the 2020s,

    Z. Liu, H. Mao, C.-Y . Wu, C. Feichtenhofer, T. Darrell, and S. Xie, “A convnet for the 2020s,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2022, pp. 11 976–11 986

    2022

  22. [30]

    Mobilevit: light-weight, general-purpose, and mobile-friendly vision transformer,

    S. Mehta and M. Rastegari, “Mobilevit: light-weight, general-purpose, and mobile-friendly vision transformer,” arXiv preprint arXiv:2110.02178, 2021

    2021 arXiv

  23. [31]

    Accessed: 24 March 2025

    iris guard. Accessed: 24 March 2025. [Online]. Available: https://www.irisguard.com

    2025

  24. [32]

    Accessed: 24 March 2025

    iris ID. Accessed: 24 March 2025. [Online]. Available: https://www.irisid.com/

    2025

  25. [33]

    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

  26. [34]

    How iris recognition works,

    J. Daugman, “How iris recognition works,” in The essential guide to image processing. Elsevier, 2009, pp. 715–739. NAVEENKUMAR G. VENKATASWAMY re- ceived a B.E. degree in Electrical and Commu- nication Engineering from Visvesvaraya Techno- logical University, Belagavi, India. ...

    2009

Pith tools

Reviewed August 15, 2026 · model on record in the stance chip above.