A Novel and Efficient Feature Extraction Method for Iris Recognition

Ko, Jong-Gook; Gil, Youn-Hee; Yoo, Ji-Sang; Chung, Kyoil

doi:10.4218/etrij.07.0206.0141

Cited by 81 publications

(35 citation statements)

References 4 publications

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“…Furthermore the algorithm by Ko et al (KO) [7] was simply adopted by allowing bigger textures without adapting the cell-size which is averaged. Note that as a result the length of the feature vector increases with the size of the texture.…”

Section: Iris-based Cs-codesmentioning

confidence: 99%

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On rotational pre-alignment for tree log identification using methods inspired by fingerprint and iris recognition

Schraml

Hofbauer

Petutschnigg

et al. 2016

Machine Vision and Applications

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Tree log end biometrics is an approach to track logs from forest to further processing companies by means of log end images. The aim of this work is to investigate how to deal with the unrestricted rotational range of cross sections in log end images. Thus, the applicability of three different rotational pre-alignment strategies in the registration procedure is assessed. Template computation and matching is based on fingerprint and iris recognition techniques which were adopted and extended to work with log end images. To address these questions, a testset built up on 279 tree logs is utilized in the experiments. The evaluation assesses the basic performance of the rotational pre-alignment strategies and their impact on the verification and identification performances for different fingerprint-and iris-based configurations. Results indicate that rotational pre-alignment in the registration procedure is the main component to deal with rotation in log end biometrics. The best configurations achieve identification rates >93 %. By showing that cross sections in log end images can be rotated to a distinctive position, this work is a first step towards real word log end biometrics.

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Section: Iris-based Cs-codesmentioning

confidence: 99%

“…Both algorithms compute a CS-Code composed of a single feature vector. Iris-based matching procedures For the matching of irisbased CS-Codes KO uses a specific comparator [7] and LG the Hamming distance. Figure 5 shows a schematic overview of iris-based rotation compensation.…”

Section: Iris-based Cs-codesmentioning

confidence: 99%

On rotational pre-alignment for tree log identification using methods inspired by fingerprint and iris recognition

Schraml

Hofbauer

Petutschnigg

et al. 2016

Machine Vision and Applications

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“…We conduct iris matching experiments using six different systems based on 1D log-Gabor filters (LG) [14], SIFT operator (SIFT) [15], local intensity variations in iris textures (CR) [16], Discrete-Cosine Transform (DCT) [17], cumulativesum-based grey change analysis (KO) [18], and Gabor spatial filters (QSW) [19]. In LG, CR, DCT, KO and QSW, the iris region is first unwrapped to a normalized rectangle using the Daugman's rubber sheet model [20].…”

Section: Iris Recognition Systemsmentioning

confidence: 99%

Reconstruction of smartphone images for low resolution iris recognition

Alonso‐Fernandez

Farrugia

Bigün

2015

2015 IEEE International Workshop on Information Forensics and Security (WIFS)

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Abstract-As iris systems evolve towards a more relaxed acquisition, low image resolution will be a predominant issue. In this paper we evaluate a super-resolution method to reconstruct iris images based on Eigen-transformation of local image patches. Each patch is reconstructed separately, allowing better quality of enhanced images by preserving local information. We employ a database of 560 images captured in visible spectrum with two smartphones. The presented approach is superior to bilinear or bicubic interpolation, specially at lower resolutions. We also carry out recognition experiments with six iris matchers, showing that better performance can be obtained at low-resolutions with the proposed eigen-patch reconstruction, with fusion of only two systems pushing the EER to below 5-8% for down-sampling factors up to a size of only 13×13.

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“…3. The third algorithm has been proposed by Ko et al [5]. Here feature extraction is performed by applying cumulative-sum-based change analysis.…”

Section: Iris Recognition and Iris Databasementioning

confidence: 99%