Pith. sign in

REVIEW 3 major objections 5 minor 26 references

Generating Realistic Forehead-Creases for User Verification via Conditioned Piecewise Polynomial Curves

T0 review · 3 major / 5 minor · reviewed 2026-08-10 · deepseek-v4-flash

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

Pith's one-line read Synthetic forehead creases, drawn as B-spline and Bezier curves and translated by a diffusion model, reduce verification error from 12.35% to 9.18% EER on a cross-database protocol.

desk verdict A reproducible geometry-guided synthetic data method for forehead crease verification with a plausible cross-database gain, but the missing model-selection specification is a load-bearing gap that needs fixing. read the letter →

arxiv 2501.13889 v1 pith:ITO7EGRD submitted 2025-01-23 cs.CV

Abhishek Tandon , Geetanjali Sharma , Gaurav Jaswal , Aditya Nigam , Raghavendra Ramachandra This is my paper · ORCID
classification cs.CV
keywords foreheadcreasesbiometricverificationsyntheticdatagenerationB-splinecurvesdiffusionmodelsedge-to-imagetranslationcross-databaseevaluationaugmentation
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 tries to establish that geometrically modelled synthetic forehead-crease identities, generated from B-spline and Bezier curve drawings and translated into images by a diffusion model, can stand in for scarce real training data in a forehead-crease verification system. The authors claim that mixing these synthetic identities with real images lowers the equal error rate from 12.35 percent (real-only training) to 9.18 percent, and raises the true-match rate at a false-match rate of 0.1 percent from 40.12 percent to 57.44 percent under a cross-database protocol. The result matters because forehead-crease data is limited and prior subject-agnostic generation without intra-subject diversity gave weaker gains. The paper's main assertion is that the combination of parametric-curve visual prompts, diffusion-based edge-to-image translation, and a staged training curriculum improves verification.

What carries the argument

The mechanism is a conditioned piecewise polynomial curve generator: principal creases are rendered with B-spline curves (piecewise polynomial curves with local control) spanning full rows of a 6x6 grid, while non-prominent creases use degree-2 Bezier curves across merged cells, with spatial positions chosen by a random grid mask. Control points are sampled and perturbed to create variants. These edge-like visual prompts condition a diffusion-based edge-to-image translation network (BBDM) trained on dilated self-quotient edge maps paired with real forehead images. Intra-subject diversity comes from control-point perturbation and visual prompt augmentations such as dropout and elastic transforms, and a curriculum that gradually adds new synthetic identity subsets prevents the recognition model from overfitting to synthetic data.

What would settle it

Retrain the FHCVS with the training curriculum but select the final model using a fixed rule such as the last training cycle or the best EER on a held-out FH-V1 split, then re-run the FH-V2' evaluation; if the EER gain over 12.35 percent vanishes, the reported improvement depends on test-set selection rather than the synthetic data.

Watch

Extended reading notes

Core claim

The central discovery is that a trait-specific generation pipeline built from B-spline principal creases and Bezier non-prominent creases, placed on a randomly masked 6x6 grid and rendered as edge maps, can serve as visual prompts for a diffusion edge-to-image translation model (BBDM). The resulting synthetic identities, diversified by perturbing control points or augmenting the prompts, provide identity-consistent mated samples. Training the FHCVS verification backbone on real plus synthetic data with a curriculum that gradually introduces new synthetic identity subsets reduces cross-database EER and improves TMR at low FMR, with the best EER at 9.18 percent for BSpline-VPD and the best true-match rates at 59.23 percent (FMR 0.1 percent) and 43.63 percent (FMR 0.01 percent) for the merged BSpline-plus-DiffEdges VPD database.

Load-bearing premise

The evaluation assumes that the best model is selected by EER on a validation set independent of the FH-V2' test database, and the paper does not state what data that EER is measured on.

Editorial extensions

If this is right

  • Synthetic BSpline-VPD data merged with real FH-V1 lowers EER to 9.18 percent versus 12.35 percent for real-only training.
  • Visual prompt augmentations contribute more to mated-sample diversity than control-point perturbation (diversity 19.198 versus 15.962) and to verification gains.
  • Diffusion-generated edge maps (DiffEdges-VPD) give comparable or better true-match rates at low false-match rates, reaching 58.49 percent at FMR 0.1 percent.
  • Merging BSpline and DiffEdges VPD identities yields the best reported true-match rates at 59.23 percent and 43.63 percent for FMR 0.1 and 0.01 percent.
  • The staged training curriculum alone improves the prior subject-agnostic synthetic method from 13.76 percent to 10.53 percent EER.

Reading between the lines

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

  • Editorial inference: if the best-model selection described in Section 4.3 uses FH-V2' or any of its subjects, the reported cross-database gains would be partly circular; a clean test would fix selection on an FH-V1 validation split before touching FH-V2'.
  • Editorial inference: the geometric-prompt-plus-diffusion recipe could transfer to other crease-based biometrics such as palmprints or knuckles, where control-point perturbation gives local identity-preserving variation.
  • Editorial inference: conditioning the Edge2FC model on texture or skin-tone descriptors, which the authors flag as future work, could close the FID gap between BSpline and DiffEdges samples while keeping B-spline local control.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 5 minor

Summary. The paper proposes a method for generating synthetic forehead-crease images: geometric visual prompts are created from B-spline and Bézier curves on a dynamic grid mask, then translated into synthetic identities by a diffusion-based edge-to-image model (Edge2FC). Two intra-subject diversity strategies are introduced: control-point perturbation (CPD) and visual-prompt augmentation (VPD). The authors also train an unconditional DDPM to generate edge maps directly. They evaluate a forehead-crease verification network (FHCVS) trained on real data plus these synthetic databases under a cross-database protocol on the FH-V2' dataset, reporting improved EER and TMR relative to training on real data alone.

Significance. If the reported gains hold, the work offers a practical solution to the limited-data problem in an emerging biometric modality, with released code and synthetic datasets that would aid reproducibility. The curriculum-based training and the comparison between geometric prompts and generative edge maps are useful experimental contributions. However, the central empirical claim is currently contingent on an unspecified model-selection step and on the absence of statistical uncertainty quantification; the claimed gains may partly reflect test-set selection or run-to-run variation rather than a genuine improvement from synthetic data.

major comments (3)
  1. [Section 4.3] The training-curriculum description states 'Finally, the best-performing model of all cycles (in terms of EER), is fine-tuned on real data' but it does not specify the data on which this EER is computed. This is load-bearing because the paper's headline result is a cross-database evaluation on FH-V2'. If the EER is computed on FH-V2' or any subset that includes its subjects, model selection has used the test set and the reported comparison (9.18% vs. 12.35% EER) is not a valid cross-database estimate. Please state the exact validation set used for cycle selection, and if necessary re-run the selection on a held-out split of FH-V1.
  2. [Table 2 and Section 5.3] No error bars, confidence intervals, or significance tests are reported for any EER or TMR value. Differences such as 9.18 vs. 9.42 vs. 9.63 EER, or 10.03 vs. 10.05, are likely within run-to-run variation given typical training stochasticity. Additionally, the real-only baseline (12.35) is not trained with the curriculum, while Experiment 1 shows that the curriculum alone changes SA-PermuteAug from 13.76 to 10.53. To attribute the improvement to synthetic data rather than to the curriculum, please include a real-only baseline trained with the same multi-cycle fine-tuning procedure.
  3. [Section 5.2 and Table 3] The abstract and title claim 'realistic' forehead-crease generation, but Table 3 reports FID scores for the BSpline datasets (162–185) that are much worse than the SA-PermuteAug baseline (56). The paper explains the discrepancy via feature-map visualization, but the fidelity claim as stated is not supported by the reported metrics. Please either temper the realism claim to reflect the actual verification-oriented objective, or provide additional evidence (e.g., human evaluation, perceptual similarity) that the synthetic images are visually realistic despite the high FID.
minor comments (5)
  1. [Section 3.3.1] The text says 'can vary from upto 6 in number'; 'upto' should be 'up to'.
  2. [Figure 1 caption] The caption says 'B-spine curves'; this should be 'B-spline curves'.
  3. [Section 5.2] The text repeatedly writes 'b-spines' (e.g., 'the synthetic samples generated using b-spines'); please use 'B-splines' consistently.
  4. [Table 2 header] The header 'TMR (%) @ FMR (%) =' repeats the percentage sign; clarify by writing e.g. 'TMR (%) at FMR = 0.1%' and 'TMR (%) at FMR = 0.01%' in separate columns.
  5. [Section 4.3] The sentence 'Here 50 is chosen to limit the total number of cycles' is vague; specify how the number of cycles is derived from the 247 synthetic IDs.

Circularity Check

0 steps flagged · score 2.0 of 10

No significant circularity; the central synthetic-to-real comparison is externally benchmarked on FH-V2', and the geometric generation pipeline does not reduce to fitted verification quantities.

full rationale

The paper's derivation chain is self-contained with respect to its central claim. The Edge2FC diffusion model is trained on real (edge, image) pairs from FH-V1 and is then frozen when translating hand-designed B-spline/Bézier visual prompts; the prompts themselves are generated from a dynamic grid mask and random control-point sampling, independent of any verification outcome. The FHCVS verification backbone is the authors' earlier ResNet-18+attention architecture [4], but it is used as a fixed architecture and trained from real and synthetic data under a curriculum; no claim rests on [4] as a uniqueness or optimality argument. The quantitative evaluation is on the real FH-V2' database, which is not used to train Edge2FC or FHCVS, so the reported EER gains are an external comparison rather than a fitted input called prediction. The one ambiguity is Section 4.3, where the best-performing training cycle is selected 'in terms of EER' without stating the data on which that EER is computed; if that EER were computed on FH-V2', the selection would leak the test set and the cross-database numbers would be partially circular. However, the paper does not state this, no equation or selection rule in the text defines the selector on FH-V2', and the released code makes the protocol checkable; the ambiguity is a reproducibility risk, not a demonstrated circular reduction. Self-citations to the authors' SA-PermuteAug work [23] appear only as a baseline dataset and comparison point, not as load-bearing evidence for the geometric model. Therefore the central derivation is not circular; at most there is a minor self-citation and an open protocol question, giving score 2.

Assumptions & free parameters 8 free parameters · 5 assumptions · 0 invented entities

The central claim rests on several domain assumptions about how well parametric curves, edge maps, and synthetic prompts represent the identity information in real forehead creases. No fitted constants are central to the verification claim, but the generative pipeline has many hand-set sampling ranges and counts. No new entities are introduced.

free parameters (8)
  • Row count distribution r ~ U(3,6) = 3 to 6
    Manually chosen to match observation that a forehead has up to 6 principal creases; controls synthetic density.
  • Principal crease row count rc ~ U(1,r) = 1 to r
    Sampled per identity; affects how many principal creases appear.
  • Non-prominent creases per merged cell w ~ U(1,3) = 1 to 3
    Sampled for each of three cells in a non-principal row; controls fine crease density.
  • B-spline degree dc ~ U(3,4) = 3 or 4
    Chosen by hand for principal crease curvature flexibility.
  • Control point perturbation magnitude m = 0.3 to 0.6
    Applied in Equations 4 and 5 to create mated samples while aiming to preserve identity.
  • Mated samples per visual prompt = 10
    One image per prompt and per perturbed prompt; number chosen without ablation.
  • Visual prompt diversity augmentations = 14
    Set of imgaug augmentations; parameters not specified and no ablation of which augmentations help.
  • Synthetic IDs per curriculum cycle = 50
    Chosen to limit cycles and computation; no sensitivity analysis.
assumptions (5)
  • domain assumption B-spline and Bezier curves on a 6 by 6 grid with a random mask represent identity-relevant forehead crease geometry.
    Sections 3.3.3 and 3.3.4 define the model from observations on FH-V1; no quantitative coverage study is provided.
  • domain assumption Dilated self-quotient edge maps preserve enough information to reconstruct a forehead image and its identity via BBDM.
    Section 3.1 justifies the edge representation with lighting robustness, but the identity-preservation claim is not measured.
  • domain assumption BBDM trained on real edge-image pairs generalizes to synthetic edge maps despite the domain gap shown by FID scores.
    Section 3.2 and Table 3; the paper notes realism is low for B-spline prompts yet verification still improves.
  • domain assumption Perturbing control points while fixing the grid mask preserves synthetic identity.
    Section 3.4.1 asserts label consistency without a matching experiment on the generated ID.
  • standard math The BBDM diffusion objective from reference [16] is correct.
    Equations 1 and 2 are imported from published work; they are not this paper's contribution.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Generating Realistic Forehead-Creases for User Verification via Conditioned Piecewise Polynomial Curves." pith.science (2026). https://pith.science/paper/ITO7EGRD

@misc{pith2026250113889,
  author       = {Pith},
  title        = {Pith review of: Generating Realistic Forehead-Creases for User Verification via Conditioned Piecewise Polynomial Curves},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/ITO7EGRD}},
  note         = {Machine review of arXiv:2501.13889}
}
read the original abstract

We propose a trait-specific image generation method that models forehead creases geometrically using B-spline and B\'ezier curves. This approach ensures the realistic generation of both principal creases and non-prominent crease patterns, effectively constructing detailed and authentic forehead-crease images. These geometrically rendered images serve as visual prompts for a diffusion-based Edge-to-Image translation model, which generates corresponding mated samples. The resulting novel synthetic identities are then used to train a forehead-crease verification network. To enhance intra-subject diversity in the generated samples, we employ two strategies: (a) perturbing the control points of B-splines under defined constraints to maintain label consistency, and (b) applying image-level augmentations to the geometric visual prompts, such as dropout and elastic transformations, specifically tailored to crease patterns. By integrating the proposed synthetic dataset with real-world data, our method significantly improves the performance of forehead-crease verification systems under a cross-database verification protocol.

Figures

Figures reproduced from arXiv: 2501.13889 by the authors.

Figure 1
Figure 1. Proposed Method: Geometrically rendered visual prompts use an Edge-to-Forehead Creases model to generate novel synthetic IDs. Principal creases are rendered using B-spine curves on j th row r c j of a 6 × 6 grid with start, control and end points: s c j , p c j , and, e c j , while non-prominent creases are plotted using Bezier curves on ´ r w j formed by merging two consecutive cells u i k. The spatial position for… view at source ↗
Figure 2
Figure 2. Edge Extraction: A forehead-creases image at each pre [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Comparison: Synthetic IDs from (a) SA-PermuteAug [ [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figures from the paper (3 more)
Figure 4
Figure 4. Figure 4: Intermediate feature maps visualization for images from [PITH_FULL_IMAGE:figures/full_fig_p007_4.png]
Figure 5
Figure 5. Figure 5: Combined DET curve for comparative analysis among [PITH_FULL_IMAGE:figures/full_fig_p007_5.png]
Figure 6
Figure 6. Figure 6: Mated samples from a synthetic ID showcasing control [PITH_FULL_IMAGE:figures/full_fig_p008_6.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

26 extracted references · 18 canonical work pages

  1. [1]

    Optimizing botulinum toxin a administra- tion for forehead wrinkles: Introducing the lines and dots 8 (lads) technique and a predictive dosage model

    Kamal Alhallak. Optimizing botulinum toxin a administra- tion for forehead wrinkles: Introducing the lines and dots 8 (lads) technique and a predictive dosage model. Toxins, 16(2):109, 2024. 3

    work page 2024

  2. [2]

    Tai- lored botulinum toxin type a injections in aesthetic medicine: consensus panel recommendations for treating the forehead based on individual facial anatomy and muscle tone

    Javier Anido, Daniel Arenas, Cristina Arruabarrena, Alfonso Dom´ınguez-Gil, Carlos Fajardo, Mar Mira, Javier Murillo, Natalia Rib´e, Helga Rivera, Sofia Ruiz del Cueto, et al. Tai- lored botulinum toxin type a injections in aesthetic medicine: consensus panel recommendations for treating the forehead based on individual facial anatomy and muscle tone. Cli...

    work page 2017

  3. [3]

    Conditional image genera- tion with score-based diffusion models

    Georgios Batzolis, Jan Stanczuk, Carola-Bibiane Sch ¨onlieb, and Christian Etmann. Conditional image genera- tion with score-based diffusion models. arXiv preprint arXiv:2111.13606, 2021. 6

    arXiv 2021

  4. [4]

    Mobile based human identification using fore- head creases: Application and assessment under covid-19 masked face scenarios

    Rohit Bharadwaj, Gaurav Jaswal, Aditya Nigam, and Kam- lesh Tiwari. Mobile based human identification using fore- head creases: Application and assessment under covid-19 masked face scenarios. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision , pages 3693–3701, 2022. 1, 3, 4, 5, 7

    work page 2022

  5. [5]

    Imagenet: A large-scale hierarchical image database

    Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. Imagenet: A large-scale hierarchical image database. In 2009 IEEE conference on computer vision and pattern recognition, pages 248–255. Ieee, 2009. 3

    2009

  6. [6]

    Diffusion models beat gans on image synthesis

    Prafulla Dhariwal and Alexander Nichol. Diffusion models beat gans on image synthesis. Advances in neural informa- tion processing systems, 34:8780–8794, 2021. 3, 5

    work page 2021

  7. [7]

    Dual attention network for scene seg- mentation

    Jun Fu, Jing Liu, Haijie Tian, Yong Li, Yongjun Bao, Zhiwei Fang, and Hanqing Lu. Dual attention network for scene seg- mentation. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 3146–3154,

    work page

  8. [8]

    Generative adversarial networks

    Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. Generative adversarial networks. Commu- nications of the ACM, 63(11):139–144, 2020. 1

    2020

Show all 26 references
  1. [9]

    Grosz and Anil K

    Steven A. Grosz and Anil K. Jain. Genpalm: Contactless palmprint generation with diffusion models. In 2024 IEEE International Joint Conference on Biometrics (IJCB), pages 1–9, 2024. 3

    2024

  2. [10]

    Deep residual learning for image recognition

    Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceed- ings of the IEEE conference on computer vision and pattern recognition, pages 770–778, 2016. 5

    2016

  3. [11]

    Gans trained by a two time-scale update rule converge to a local nash equilib- rium

    Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter. Gans trained by a two time-scale update rule converge to a local nash equilib- rium. Advances in neural information processing systems , 30, 2017. 6

    2017

  4. [12]

    Palmprint verification based on robust line orientation code

    Wei Jia, De-Shuang Huang, and David Zhang. Palmprint verification based on robust line orientation code. Pattern Recognition, 41(5):1504–1513, 2008. 3

    2008

  5. [13]

    Pce-palm: Palm crease energy based two-stage real- istic pseudo-palmprint generation

    Jianlong Jin, Lei Shen, Ruixin Zhang, Chenglong Zhao, Ge Jin, Jingyun Zhang, Shouhong Ding, Yang Zhao, and Wei Jia. Pce-palm: Palm crease energy based two-stage real- istic pseudo-palmprint generation. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 38...

    2024

  6. [14]

    Adaface: Quality adaptive margin for face recognition

    Minchul Kim, Anil K Jain, and Xiaoming Liu. Adaface: Quality adaptive margin for face recognition. In Proceed- ings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022. 5, 7

    2022

  7. [15]

    Recovering and match- ing minutiae patterns from finger knuckle images

    Ajay Kumar and Bichai Wang. Recovering and match- ing minutiae patterns from finger knuckle images. Pattern Recognition Letters, 68:361–367, 2015. 3

    2015

  8. [16]

    Bbdm: Image- to-image translation with brownian bridge diffusion models

    Bo Li, Kaitao Xue, Bin Liu, and Yu-Kun Lai. Bbdm: Image- to-image translation with brownian bridge diffusion models. In Proceedings of the IEEE/CVF conference on computer vi- sion and pattern Recognition, pages 1952–1961, 2023. 3

    1952

  9. [17]

    Focal loss for dense object detection

    Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He, and Piotr Doll´ar. Focal loss for dense object detection. In Pro- ceedings of the IEEE international conference on computer vision, pages 2980–2988, 2017. 5, 7

    2017

  10. [18]

    Fine-tuning diffusion models with limited data

    Taehong Moon, Moonseok Choi, Gayoung Lee, Jung-Woo Ha, and Juho Lee. Fine-tuning diffusion models with limited data. In NeurIPS 2022 Workshop on Score-Based Methods,

    2022

  11. [19]

    Shape in- terrogation for computer aided design and manufacturing , volume 15

    Nicholas M Patrikalakis and Takashi Maekawa. Shape in- terrogation for computer aided design and manufacturing , volume 15. Springer, 2002. 4

    2002

  12. [20]

    High-resolution image synthesis with latent diffusion models

    Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Bj ¨orn Ommer. High-resolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 10684–10695, 2022. 1

    2022

  13. [21]

    Fh-sstnet: Forehead creases based user verification using spatio-spatial temporal net- work

    Geetanjali Sharma, Gaurav Jaswal, Aditya Nigam, and Raghavendra Ramachandra. Fh-sstnet: Forehead creases based user verification using spatio-spatial temporal net- work. In 2024 12th International Workshop on Biometrics and Forensics (IWBF), pages 1–6, 2024. 1

    2024

  14. [22]

    Rpg-palm: Realistic pseudo-data generation for palmprint recognition

    Lei Shen, Jianlong Jin, Ruixin Zhang, Huaen Li, Kai Zhao, Yingyi Zhang, Jingyun Zhang, Shouhong Ding, Yang Zhao, and Wei Jia. Rpg-palm: Realistic pseudo-data generation for palmprint recognition. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages...

    2023

  15. [23]

    Synthetic forehead-creases biometric generation for reliable user ver- ification

    Abhishek Tandon, Geetanjali Sharma, Gaurav Jaswal, Aditya Nigam, and Raghavendra Ramachandra. Synthetic forehead-creases biometric generation for reliable user ver- ification. In 2024 IEEE International Joint Conference on Biometrics (IJCB), pages 1–9. IEEE, 2024. 1, 5, 6, 7

    2024

  16. [24]

    Eca-net: Efficient channel at- tention for deep convolutional neural networks

    Qilong Wang, Banggu Wu, Pengfei Zhu, Peihua Li, Wang- meng Zuo, and Qinghua Hu. Eca-net: Efficient channel at- tention for deep convolutional neural networks. In Proceed- ings of the IEEE/CVF conference on computer vision and pattern recognition, pages 11534–11542, 2020. 5

    2020

  17. [25]

    Image quality assessment: from error visibility to structural similarity

    Zhou Wang, Alan C Bovik, Hamid R Sheikh, and Eero P Si- moncelli. Image quality assessment: from error visibility to structural similarity. IEEE transactions on image processing, 13(4):600–612, 2004. 6

    2004

  18. [26]

    B ´ezierpalm: A free lunch for palmprint recognition

    Kai Zhao, Lei Shen, Yingyi Zhang, Chuhan Zhou, Tao Wang, Ruixin Zhang, Shouhong Ding, Wei Jia, and Wei Shen. B ´ezierpalm: A free lunch for palmprint recognition. In European Conference on Computer Vision, pages 19–36. Springer, 2022. 2, 3, 8 9

    2022

Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.