Pith. Sign in

RECORD

Iris Recognition Using Scattering Transform and Textural Features

PaperPDF from arXiv

data files

Paper breakdownThe authors' claims, conditions and measurements, as JSON Signed receiptProof this review has not been altered
X Bluesky LinkedIn Reddit Hacker News
Shervin Minaee, AmirAli Abdolrashidi, Yao Wang

Computer Vision and Pattern Recognition · Submitted 8 Jul 2015

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim The proposed scheme is tested on a well-known iris database, and showed promising results with the best accuracy rate of 99.2%.

how it is stated · asserted 0.96 read the record →

Comment (0) Read on arXiv ↗
Cite ▾
arXiv ID1507.02177 (version 1)
Pith IDSBFGIE54, Pith's permanent record of this paper and its review
CitationAmirAli Abdolrashidi, Shervin Minaee, Yao Wang. "Iris Recognition Using Scattering Transform and Textural Features." arXiv:1507.02177 (2015). https://pith.science/paper/SBFGIE54
Claim paper

The compiled record

The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.

Open the compiled record

Compiled by classifier-1.13-20260917 via OpenRouter, question bank abstract-p1-20260923, 2026-09-23: 1 request, 5,397 input tokens, $0.0002 reported.

What the abstract itself states, as probabilities: claims first 0.04 · states limitation 0.05 · human participants 0.39 · controlled comparison 0.05 · randomized assignment 0.01 · states quantitative result 0.99 · compares with prior results 0.11 · mentions code or data availability 0.02.

The abstract, sentence by sentence

arXiv listing abstract v1. Results and the main claim are underlined; .

Iris recognition has drawn a lot of attention since the mid-twentieth century. Among all biometric features, iris is known to possess a rich set of features. Different features have been used to perform iris recognition in the past. In this paper, two powerful sets of features are introduced to be used for iris recognition: scattering transform-based features and textural features. PCA is also applied on the extracted features to reduce the dimensionality of the feature vector while preserving most of the information of its initial value. Minimum distance classifier is used to perform template matching for each new test sample. The proposed scheme is tested on a well-known iris database, and showed promising results with the best accuracy rate of 99.2%.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 Iris recognition has drawn a lot of attention since the mid-twentieth century. background 1.00 0.04 0.01 asserted 1.00
s1 Among all biometric features, iris is known to possess a rich set of features. background 1.00 0.05 0.02 asserted 1.00
s2 Different features have been used to perform iris recognition in the past. background 1.00 0.03 0.02 asserted 0.99
s3 In this paper, two powerful sets of features are introduced to be used for iris recognition: scattering transform-based features and textural features. contribution 0.99 0.88 0.08 asserted 1.00
s4 PCA is also applied on the extracted features to reduce the dimensionality of the feature vector while preserving most of the information of its initial value. method 1.00 0.89 0.02 asserted 1.00
s5 Minimum distance classifier is used to perform template matching for each new test sample. method 1.00 0.84 0.02 asserted 1.00
s6 The proposed scheme is tested on a well-known iris database, and showed promising results with the best accuracy rate of 99.2%. result 1.00 0.96 0.89 asserted 0.96

What kind of paper the classifier reports this is: method or tool 0.99 · evidence from analysis of existing data 0.51.

What was examined, and what was not

sources read
  • arXiv listing abstract v1
not examined
  • full text
  • figures and tables
  • references and citing sentences
  • relations to other papers
levels run
  • P abstract
not run
  • S sections
  • T body sentences
  • C citations
  • R cross-paper relations

Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

Iris recognition has drawn a lot of attention since the mid-twentieth century. Among all biometric features, iris is known to possess a rich set of features. Different features have been used to perform iris recognition in the past. In this paper, two powerful sets of features are introduced to be used for iris recognition: scattering transform-based features and textural features. PCA is also applied on the extracted features to reduce the dimensionality of the feature vector while preserving most of the information of its initial value. Minimum distance classifier is used to perform template matching for each new test sample. The proposed scheme is tested on a well-known iris database, and showed promising results with the best accuracy rate of 99.2%.

Human review

How Pith checks papers → · Full audit of this paper →

How the literature uses it

What this paper does with the arXiv papers it cites

Each citing sentence is taken from the citing paper's LaTeX source and typed by Pith's classifier. A label appears at probability 0.8 or more (0.95 for support), a contest at 0.7 or more; the quoted words are the citing authors'. How each label was checked · JSON

Discussion

Know something about this paper? Leave a comment, or highlight any passage above to comment on it.