Multilinear principal component analysis for iris biometric system

Kamlaskar, Chetana; Abhyankar, Aditya

doi:10.11591/ijeecs.v23.i3.pp1458-1469

Cited by 6 publications

(5 citation statements)

References 24 publications

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“…The actual acceptance rate (GAR) for the face was 87.33%, 71.55% for fused irises, and 94.44% for all three fused irises. For more detiales see [13], [14].…”

Section: Iris and The Face Mergementioning

confidence: 99%

Biometric face recognition method using graphics processing unit system

Alshiha

Al-Neama

Al-Qubaa

2023

IJEECS

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The expansion of biometric applications and databases is worrying. Processing extensive or sophisticated biometric data results in longer wait times, which might restrict application usefulness. This work focuses on accelerating the processing of biometric data and proposes a parallel method of data processing that exceeds the capabilities of a central processing unit (CPU). The combination of the graphics processing unit (GPU) and compute unified device architecture (CUDA) results in at least three times the processing speed of a published accurate and secure multimodal biometric system. The GPU-assisted approach beats the CPU-only implementation when saturating the CPU-only performance with more people than the available thread count. The GPU-assisted solution is also proven to have the same accuracy as the original system, indicating accuracy and processing performance improvements in the demanding big data environment.

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“…The actual acceptance rate (GAR) for the face was 87.33%, 71.55% for fused irises, and 94.44% for all three fused irises. For more detiales see [13], [14].…”

Section: Iris and The Face Mergementioning

confidence: 99%

Biometric face recognition method using graphics processing unit system

Alshiha

Al-Neama

Al-Qubaa

2023

IJEECS

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“…Circular iris region is transformed into a fixed size rectangular block. Daugman's rubber-sheet model is considered as the most used method for iris normalization (transform iris from Cartesian to polar representation) [32]. For each pixel in the iris image, we can identify an equivalent position on the polar axes (r,ϴ) where r is the radial distance and ϴ is the rotation angle (2).…”

Section: Normalizationmentioning

confidence: 99%

Fusion of Gabor filter and steerable pyramid to improve iris recognition system

Radouane

Zouggari

Amraoui

et al. 2022

IJ-AI

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Iris recognition system is a technique of identifying people using their distinctive features. Generally, this technique is used in security, because it offers a good reliability. Different researchers have proposed new methods for iris recognition system to increase its effectiveness. In this paper, we propose a new method for iris recognition based on Gabor filter and steerable pyramid decomposition. It’s an efficient and accurate linear multi-scale, multi-orientation image decomposition to capture texture details of an image. At first, the iris image is segmented, normalized and decomposed by Gabor filter and steerable pyramid method. Multiple sub-band are generated by applying steerable pyramid on the input image. High frequency sub-band is ignored to eliminate noise and increase the accuracy. The method was validated using CASIA-v4 (Chinese Academy of Sciences Institute of Automation), IITD (Indian Institute of Technology Delhi) and UPOL (University of Phoenix Online) databases. The performance of the proposed method is better than the most methods in the literature. The proposed algorithm provides accuracy of 99.99%. False acceptance rate (FAR), equal error rate (EER) and genuine acceptance rate (GAR) have also been improved.

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“…Principal component analysis (PCA) [25] is an unsupervised technique based on simple linear transformation, it is a dimensionality reduction technique [26]. However, the main goal of a PCA is to compress data.…”

Section: Principal Component Analysismentioning

confidence: 99%

End-to-end deep auto-encoder for segmenting a moving object with limited training data

Kebir¹,

Taibi²

2022

IJECE

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Deep learning-based approaches have been widely used in various applications, including segmentation and classification. However, a large amount of data is required to train such techniques. Indeed, in the surveillance video domain, there are few accessible data due to acquisition and experiment complexity. In this paper, we propose an end-to-end deep auto-encoder system for object segmenting from surveillance videos. Our main purpose is to enhance the process of distinguishing the foreground object when only limited data are available. To this end, we propose two approaches based on transfer learning and multi-depth auto-encoders to avoid over-fitting by combining classical data augmentation and principal component analysis (PCA) techniques to improve the quality of training data. Our approach achieves good results outperforming other popular models, which used the same principle of training with limited data. In addition, a detailed explanation of these techniques and some recommendations are provided. Our methodology constitutes a useful strategy for increasing samples in the deep learning domain and can be applied to improve segmentation accuracy. We believe that our strategy has a considerable interest in various applications such as medical and biological fields, especially in the early stages of experiments where there are few samples.

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Multilinear principal component analysis for iris biometric system

Cited by 6 publications

References 24 publications

Biometric face recognition method using graphics processing unit system

Biometric face recognition method using graphics processing unit system

Fusion of Gabor filter and steerable pyramid to improve iris recognition system

End-to-end deep auto-encoder for segmenting a moving object with limited training data

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