Iris Recognition System Using PCA Based on DWT

Rana, Humayan Kabir; Azam, Md. Shafiul; Akhtar, Mst. Rashida

doi:10.36876/smjbb.1015

Cited by 7 publications

(3 citation statements)

References 5 publications

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“…Recognition Rate (%) H. Huma et al [17] 89.99 A. R. Hashim et al [15] 91.139 M.S. Azam et al [11] 92.6 SG Firake et al [10] 92.85 Y. A. Jasim et al [14] 93.98 M. H. Hamd et al [12] 94 MH Hamd et al [13] 94 HK Rana et al [16] 95.4 Our proposed 95.5…”

Section: Methodsmentioning

confidence: 99%

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Efficient Method for Iris Recognition System

2024

wjps

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This article shows and tests a good eye recognition method that works well in image situations with fewer restrictions. The Circular Hough Transform (CHT) and Truncated Total Variation model were used to separate the iris from other parts and noises in an eye picture to locate and separate the iris accurately. This helps get the most exact pictures of the eye. Doughman’s rubber sheet model makes the split eye area regular and normalized. The Principal Component Analysis (PCA) method is used to identify features of iris patterns that come from Eigen irises. A test image is projected onto the subspace occupied by the eigen iris to do recognition. Then, recorded eigen irises are compared with the test image by finding the normalized hamming distance between them. Up to 95.5% of the accurate, the planned method has given correct findings.

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Section: Methodsmentioning

confidence: 99%

“…The authors in [11] proposed a technique using PCA based on DWT to select iris template features and increasing iris recognition efficiency. Used converting the iris image into one frequency band and applying PCA for feature extraction and Euclidean Distance for classification.…”

mentioning

confidence: 99%

Efficient Method for Iris Recognition System

2024

wjps

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“…Therefore, it can prove to be an excellent feature to be used for distinguishing identical twins in the process of biometric identification and digital passport management. Hence, this drawback can be addressed using detection of iris by integrating feature extraction and Principal Component Analysis (PCA) or by using the watermarkingbased approach [32][33][34][35].…”

Section: Future Scopementioning

confidence: 99%

A Proposed Framework for Identity Verification in Passport Management Using Model Scaling and Semantic Similarity

Khurana,

Bhatia,

Battan

et al. 2023

ISI

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The dependence on images for face detection and identity verification was at odds with broad assessment which showed that coordinating with sets of non-similar images was profoundly inclined to make mistakes. The current passport management lacks security of data and requires more manpower and time, manual calculations, and verification of documents as well as the photographs. Additionally, the computerized passport examination requires accurate processing in order to carry out crucial tasks like identifying passport forgeries, looking for wanted criminals, or finding those who are ineligible for immigration, among other things because the utilization of fake passports addresses a huge danger to the security of the nation. Therefore, the purpose of this paper is to enhance the identity verification process in passport management. The paper comprises implementation of various technologies and technology driven models for serving its purpose by using the latest and efficient techniques that includes neural networks, face detection, image similarity, ROI (region of interest) and model scaling. The dataset utilized in the paper was self-prepared by the authors and consisted of 5 images each for 10 different individuals. It had been trained from scratch on different models based on CNN architecture. Out of all the three models used for training, the model that resulted in highest accuracy was further considered for research. EfficientNetB0 resulted in 82% accuracy being the highest out of all the three models. The threshold for the model was thereby calculated using z-score which compared the similarity scores and classified the images as similar or non-similar. Therefore, it can be concluded that the techniques utilized in the paper are efficient enough to determine the similarity of an image with its corresponding image.

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