pith. sign in

arxiv: 2605.17673 · v1 · pith:QJYX6KTMnew · submitted 2026-05-17 · 💻 cs.CV

A simple approach for biometrics: Finger-knuckle prints recognition based on a Sobel filter and similarity measures

E. O. Rodrigues , T. M. Porcino , Aura Conci , Aristofanes C. Silva This is my paper

Pith reviewed 2026-05-20 13:23 UTC · model grok-4.3

classification 💻 cs.CV
keywords finger knuckle printbiometricsSobel filteredge detectionsimilarity measuresbinary imagesrecognition
0
0 comments X

The pith

Finger-knuckle prints can be recognized by comparing binary edge maps from a Sobel filter using simple similarity measures.

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

The paper introduces a method for finger-knuckle print recognition that relies on basic image processing rather than complex learned features. It applies the Sobel operator to detect edges in photos of the finger-knuckle region, followed by noise reduction to produce binary images. These binary representations are then matched against a database using standard similarity measures through direct one-to-one comparisons. The approach reports a true positive rate reaching 17.02 percent on a large dataset. This demonstrates that straightforward edge-based templates can support biometric identification tasks.

Core claim

The central claim is that a Sobel filter for edge detection, combined with simple noise reduction to create binary images, enables finger-knuckle print recognition when those images are compared using similarity measures, yielding up to 17.02 percent true positives on a large dataset.

What carries the argument

Sobel operator edge detection that converts knuckle images to binary maps for direct comparison via similarity measures.

If this is right

  • Biometric matching can proceed via exhaustive pairwise comparison of preprocessed binary images without training classifiers.
  • The resulting binary images reduce storage needs and speed up processing for large collections of prints.
  • Off-the-shelf similarity metrics become sufficient for identification once edges are isolated by the filter.
  • The pipeline scales to sizable datasets while remaining computationally light.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Similar edge-map comparisons could extend to other contactless biometrics such as palm or wrist vein patterns.
  • Performance gaps might close by testing combinations of multiple similarity measures on the same binary outputs.
  • The method implies that some biometric tasks do not require deep feature learning when domain-specific edge structure is strong.

Load-bearing premise

That direct comparison of Sobel-preprocessed binary images via off-the-shelf similarity measures is sufficient to produce usable identification rates without additional learned features or class-specific modeling.

What would settle it

A test on a new dataset with controlled variations in finger rotation, scale, or lighting that causes the true positive rate to drop below usable levels.

read the original abstract

The objective of this work is to propose a novel methodology for the finger knuckle print recognition, which is essentially a digital photo of the finger-knuckle region. We have employed very simple concepts of visual computing such as a filter based on the Sobel operator for finding edges and a simple noise reduction algorithm. These operations are exceptionally fast and produce binary images, which are very efficient to process and to store. Furthermore, alongside this preprocessing, some similarity measures were also regarded and evaluated for the task. After preprocessing an input finger it is compared to all the images of fingers in the dataset, one by one. We have obtained up to 17.02% of successful recognitions (true positive rate) with a large dataset.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The paper presents a simple finger-knuckle print recognition method that applies the Sobel operator for edge detection followed by noise reduction to generate binary images, then ranks matches using standard similarity measures. The central empirical claim is a true-positive identification rate of up to 17.02% on a large dataset.

Significance. If the reported rate were shown to be robust under controlled conditions with proper baselines, the work would illustrate that elementary edge-based binary representations can yield modest identification performance at very low computational cost. However, the absolute performance level is low for biometric identification tasks, and the absence of supporting experimental details substantially reduces the potential contribution.

major comments (2)
  1. Abstract: the claim of obtaining 'up to 17.02% of successful recognitions (true positive rate) with a large dataset' supplies no dataset size, number of subjects, number of probe-gallery pairs, matching protocol, or description of how the percentage was calculated, leaving the central empirical result unsupported by visible evidence.
  2. Method description (preprocessing and comparison steps): direct comparison of Sobel-derived binary edge maps is performed without any registration, alignment, or normalization step for translation, rotation, or scale; given that finger-knuckle images are known to exhibit placement variability, this omission risks the similarity scores being dominated by alignment artifacts rather than identity-specific texture.
minor comments (1)
  1. The abstract and method sections would benefit from explicit statements of the similarity measures employed and any thresholding or decision rules used to declare a match.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments on our manuscript. We address each major point below and describe the revisions we will make.

read point-by-point responses

  1. Referee: Abstract: the claim of obtaining 'up to 17.02% of successful recognitions (true positive rate) with a large dataset' supplies no dataset size, number of subjects, number of probe-gallery pairs, matching protocol, or description of how the percentage was calculated, leaving the central empirical result unsupported by visible evidence.

    Authors: We agree that the abstract lacks sufficient supporting details on the experimental setup. The full manuscript describes the dataset and protocol in the experimental section, but we acknowledge the abstract should be self-contained. In the revised version we will expand the abstract to state the dataset size, number of subjects, number of probe-gallery pairs, the identification protocol, and the precise definition used for the true-positive rate. revision: yes

  2. Referee: Method description (preprocessing and comparison steps): direct comparison of Sobel-derived binary edge maps is performed without any registration, alignment, or normalization step for translation, rotation, or scale; given that finger-knuckle images are known to exhibit placement variability, this omission risks the similarity scores being dominated by alignment artifacts rather than identity-specific texture.

    Authors: This concern is valid. Our method intentionally omits explicit registration to preserve computational simplicity and low cost, as stated in the paper. Nevertheless, we recognize that placement variability may influence the scores. In the revision we will expand the method description to detail the preprocessing and comparison pipeline and add a short discussion of this limitation, noting that alignment artifacts could affect results and that future extensions could incorporate registration. revision: partial

Circularity Check

0 steps flagged

No circularity: purely experimental reporting of matching rates on preprocessed images

full rationale

The paper presents a straightforward pipeline: Sobel edge detection plus noise reduction to create binary images, followed by ranking via off-the-shelf similarity measures. The central result (17.02% true-positive identification rate) is an empirical outcome measured on a held-out dataset, not obtained by fitting parameters to the target metric or by any equation that reduces to the authors' own definitions. No derivation chain, uniqueness theorems, or self-citations are used to justify the method; the work is self-contained as an experimental report without load-bearing mathematical reductions.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The approach rests on the domain assumption that knuckle skin patterns are adequately captured by edge information alone and that generic similarity measures will discriminate identities in a large set.

axioms (1)
  • domain assumption Sobel edge detection plus simple noise reduction produces binary images that preserve identity-discriminating knuckle patterns
    Invoked in the preprocessing description as the basis for efficient matching.

pith-pipeline@v0.9.0 · 5669 in / 1219 out tokens · 71994 ms · 2026-05-20T13:23:55.447787+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

What do these tags mean?
matches
The paper's claim is directly supported by a theorem in the formal canon.
supports
The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends
The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
uses
The paper appears to rely on the theorem as machinery.
contradicts
The paper's claim conflicts with a theorem or certificate in the canon.
unclear
Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.

Reference graph

Works this paper leans on

9 extracted references · 9 canonical work pages

  1. [1]

    C.C.Y. Poon, Y. T. Zhang and S. D. Bao, A novel biometrics method to secure wireless body area sensor networks for telemedicine and m-health, Communications Magazine, v. 44, n. 4, pp. 73-81, 2006. R. Snelick, U. Uludag, A. Mink and M. Indovina, Large-scale evaluation of multimodal biomeiric authentication using siate-of-the-art sysiems, IEEE Transactions ...

    work page 2006

  2. [2]

    Zhang, Finger-knuckle-print: A new biometric idenifier, Intemational Conference on Image Processing (ICIP), pp

    L. Zhang, Finger-knuckle-print: A new biometric idenifier, Intemational Conference on Image Processing (ICIP), pp. 1981-1984, 2009

    work page 1981

  3. [3]

    Le-qing, Finger knuckle print recognition based on SURF algorithm, Intenational Conference on Fuzzy Systems and Knowledge Discovery (FSKD), pp

    Z. Le-qing, Finger knuckle print recognition based on SURF algorithm, Intenational Conference on Fuzzy Systems and Knowledge Discovery (FSKD), pp. 1879-1883, 2011

    work page 2011

  4. [4]

    Zhang, L

    L. Zhang, L. Zhang, D. Zhang and Z. Guoc, Phase congruency induced local features for finger-knuckle-print recognition, Pattem Recognition, pp. 2522-2531, 2012

    work page 2012

  5. [5]

    D. S. Guru, K. B. Nagasundara and S. Manjunath, Fearure level Jusion of multi-instance finger knuckle print for person identification, Proceedings of the First Intemmational Conference on Intelligent Interactive Technologies and Multimedia, pp. 186-190, 2009

    work page 2009

  6. [6]

    History and Definition of the so-called Sobel Operator, more appropriately named the Sobel-Feldman Operator, 2014

    L Sobel. History and Definition of the so-called Sobel Operator, more appropriately named the Sobel-Feldman Operator, 2014. Avail- able: hitp://www.researchgate.net/publication/239398674_An_sotropic_ 3_3_Image_Gradient_Operator/ O. Rodrigues and E. Clua, A Real Time Lighting Technique for Procedurally Generated 2D Isometric Game Terrains, Entertainment Co...

    work page arXiv 2014

  7. [7]

    E. O. Rodrigues, EEC. Morais, N.A.O.S. Morais, L. Conci, L.V. Neto and A. Conci, A novel approach for the automated segmentation of cardiac fats on computed tomography, Computer Methods and Programs in Biomedicine, v. 123, pp. 109-128, 2016. [10] W. R. Hamming, Error detecting and error correcting codes. Bell System Technical Journal, v. 29, n. 2, pp. 147...

    work page 2016

  8. [8]

    Conci, S.L

    A. Conci, S.L. Galvo, G.O. Sequeiros, DM. Saade and T. MacHenry, A new measure for comparing biomedical regions of interest in segmentation of digital images, Discrete Appl. Math., v. 197, pp. 103-113, 2015

    work page 2015

  9. [9]

    Thayananthan, B

    A. Thayananthan, B. Stenger, PH.S. Torr and R. Cipolla, Shape Context and Chamfer Matching in Cluitered Scenes, Proceedings of Computer Vision and Pattern Recognition, v. 1, pp. 127-133, 2003. R

    work page 2003