Improved Cross-Spectral Iris Matching Using Multi-Scale Weberface and Gabor Local Binary Pattern

Oktiana, Maulisa; Saddami, Khairun; Arnia, Fitri; Away, Yuwaldi; Munadi, Khairul

doi:10.1109/ecai.2018.8679097

Cited by 7 publications

(4 citation statements)

References 14 publications

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“…They suggested Maulisa Oktiana et al [11], A method based on Multi-Scale Weberface (MSW) crossspectral for iris matching, which combines Gabor with Local Binary Pattern (GLBP) integration with MSW. They also used the CHT technique to divide both (VIS and NIR ) iris images.…”

Section: Related Workmentioning

confidence: 99%

Iris Recognition in Cross-Spectral Based on Histogram of Oriented Gradient and Linear Discriminant Analysis

AL-kardhi,

Al-juboori

2023

JQCM

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The use of biological and behavioural characteristics has significantly increased in recent years. Like fingerprints, faces, iris, and others in numerous crucial applications in security and electronic governance. Where recently, Research has concentrated on illuminating the recognition of iris images obtained in different fields across spectra by sensors that can capture double iris images in various areas (environment/lighting) by infrared Nearby and visible illumination (NIR, VIS) due to features unique in the iris texture that differ for the same individual, between their left and right eyes. But there is challenging to match NIR and VIS photographs due to the spectrum differences between pictures captured by near-infrared and visible light. We suggest utilizing the Histogram of Oriented Gradient (HOG) approach to extract meaningful information from images of the iris and implement Normalization to reduce differences in illumination, also apply the LDA for dimensionality reduction and K-Nearest Neighbor and Support Vector Machine as a classifier. The experimental findings were implemented on the PolyU bi-spectral dataset, and the results show that the suggested approach achieved exceptional matching performance by KNN, SVM on (Right to Left) for each (NIR, VIS) or versa (99.14%,99.04%, 97.44%,96.38%) respectively.

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Section: Related Workmentioning

confidence: 99%

Iris Recognition in Cross-Spectral Based on Histogram of Oriented Gradient and Linear Discriminant Analysis

AL-kardhi,

Al-juboori

2023

JQCM

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“…Figure 1 illustrates the face recognition scheme in (a) CS and (b) CSCD frameworks. CS matching refers to the matching of two face images captured under different spectra to provide a more accurate facial description [ 30 , 31 ]. In the CS system, the face images captured under the NIR spectral band are matched with the face images captured under the VIS spectral band.…”

Section: Related Workmentioning

confidence: 99%

Homomorphic Filtering and Phase-Based Matching for Cross-Spectral Cross-Distance Face Recognition

Arnia

Oktiana

Saddami

et al. 2021

Sensors

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Facial recognition has a significant application for security, especially in surveillance technologies. In surveillance systems, recognizing faces captured far away from the camera under various lighting conditions, such as in the daytime and nighttime, is a challenging task. A system capable of recognizing face images in both daytime and nighttime and at various distances is called Cross-Spectral Cross Distance (CSCD) face recognition. In this paper, we proposed a phase-based CSCD face recognition approach. We employed Homomorphic filtering as photometric normalization and Band Limited Phase Only Correlation (BLPOC) for image matching. Different from the state-of-the-art methods, we directly utilized the phase component from an image, without the need for a feature extraction process. The experiment was conducted using the Long-Distance Heterogeneous Face Database (LDHF-DB). The proposed method was evaluated in three scenarios: (i) cross-spectral face verification at 1m, (ii) cross-spectral face verification at 60m, and (iii) cross-spectral face verification where the probe images (near-infrared (NIR) face images) were captured at 1m and the gallery data (face images) was captured at 60 m. The proposed CSCD method resulted in the best recognition performance among the CSCD baseline approaches, with an Equal Error Rate (EER) of 5.34% and a Genuine Acceptance Rate (GAR) of 93%.

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“…In structural matching, the corresponding pixels of NIR and VIS images are used to describe the structure of an iris pattern. Oktiana et al [28] extracted iris texture using the multi-scale Weberface and Gabor local binary pattern (MGLBP) texture feature generator. Other authors explored the integration of texture features with various feature descriptors.…”

Section: Related Workmentioning

confidence: 99%

Cross-spectral iris recognition using phase-based matching and homomorphic filtering

Oktiana

Horiuchi

Hirai

et al. 2020

Heliyon

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In cross-spectral iris recognition, different spectral bands are used to obtain rich information of the human iris. Previous studies on cross-spectral iris recognition are based primarily on feature-based approaches, which are prone to the changes in parameters in the feature extraction process, such as spatial position and iris image acquisition conditions. These parameters can degrade iris recognition performance. In this paper, we present a phase-based approach for cross-spectral iris recognition using phase-only correlation (POC) and band-limited phase-only correlation (BLPOC). A phase-based iris recognition system recognizes an iris using the phase information contained in the iris image; therefore, its performance is not affected by feature extraction parameters. However, the performance of a phase-based cross-spectral iris recognition is strongly influenced by specular reflection. Different illumination conditions may produce different iris images from the same subject. To overcome this challenge, we integrate a photometric normalization technique -homomorphic filteringwith phasebased cross-spectral iris recognition. The experimental results reveal that the proposed technique achieved an excellent matching performance with an equal error rate of 0.59% and a genuine acceptance rate of 95%.

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Improved Cross-Spectral Iris Matching Using Multi-Scale Weberface and Gabor Local Binary Pattern

Cited by 7 publications

References 14 publications

Iris Recognition in Cross-Spectral Based on Histogram of Oriented Gradient and Linear Discriminant Analysis

Iris Recognition in Cross-Spectral Based on Histogram of Oriented Gradient and Linear Discriminant Analysis

Homomorphic Filtering and Phase-Based Matching for Cross-Spectral Cross-Distance Face Recognition

Cross-spectral iris recognition using phase-based matching and homomorphic filtering

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