A Proposed Biometric Technique for Improving Iris Recognition

Farouk, Rahmatallah Hossam; Mohsen, Heba; El-Latif, Yasser M. Abd

doi:10.1007/s44196-022-00135-z

Cited by 9 publications

(5 citation statements)

References 18 publications

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“…Although this method is considered more accurate and secure than RFID, time and cost limitations remain. The same holds for the iris recognition-based attendance registration system [8]. Using face recognition algorithms, scientists have automated marking attendance because no other system could perform the task quickly and accurately [9].…”

Section: Related Workmentioning

confidence: 94%

Facial Recognition Technology-Based Attendance Management System Application in Smart Classroom

Lateef,

Kamil

2023

ijcsm

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Traditional attendance recording methods can be replaced with artificial intelligence (AI) methods. This paper aims to develop an automated attendance system with a user-friendly graphical interface in Arabic to enhance user interaction. The Python programming language is used to build the system, which employs object detection and feature extraction algorithms. This system processes a live broadcast from an IP camera installed in the classroom. The system employs object detection algorithms to detect the faces in the video. The face locations are then sent to the feature extraction algorithms to extract and compare features with the features stored in the database. Our paper concludes that automating the attendance registration process using facial recognition technology can achieve up to 100% accuracy. Comparing our proposed system with previous studies, we find that it outperforms them in several aspects. Our method handles real-time tests with a larger number of students. It accommodates students positioned at different distances from the camera within the classroom, demonstrating our method's effectiveness in automating the attendance registration process.

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Section: Related Workmentioning

confidence: 94%

Facial Recognition Technology-Based Attendance Management System Application in Smart Classroom

Lateef,

Kamil

2023

ijcsm

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“…This paper [11] enhanced the convolution deep learning model (ICDLM) to control the patient attributes in the hospital. In addition to the convolutional neural network (CNN) and Hamming Distance (HD), a new hybrid approach for edge detection and segmentation, and feature extraction and classi cation, was introduced [12]. The paper [13] provided type-2 fuzzy blended enhanced Evidence D-S (DLF) combination rule for multisensory data fusion that eliminates paradoxes of the Dempster-Shafer (D-S) combination rule.…”

Section: Related Workmentioning

confidence: 99%

Improved Human Identification by Multi-biometric Image Sensor Integration With a Deep Learning Approach

Amin,

Gantra

et al. 2024

Preprint

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Biometric identification technology has become a common part of daily life due to the global demand for information security and security legislation. Due to its capacity to circumvent several fundamental drawbacks of unimodal biometric systems, multimodal biometrics technology has attracted attention and grown in popularity in this respect. This research presents a novel multimodal biometric person identification system based on a VGG19 with softmax classifier (VGG19-SC) for iris and facial biometrics. The system's architecture is built on VGG19-SC, which extracts features from and categorizes images. The system was created by combining the iris and face portions of two VGG19-SC models. VGG-19 was employed to construct the well-known pertained model. A few methods, including picture augmentation and dropout techniques, were used to prevent overfitting. The VGG19-SC models were fused using feature-level and score-level fusion methods to investigate the effects of these fusion methods on recognition performance. The results demonstrated that three biometric features were more effective than two and one biometric traits in biometric identification systems. The findings similarly demonstrated the suggested method easily surpassed other cutting-edge approaches by obtaining an accuracy of 99.39% in a multi-biometric verification system.

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“…If a match is found, the individual's identity is verified or recognized. Otherwise, if there is no match, the samples are considered as not matched (5) .…”

Section: Fig 2 Fusion Levels In Multimodal Biometric Systemsmentioning

confidence: 99%

“…ORL and FERET datasets for face, and CASIAv3.0 for iris, are used. (5) performs both matching score and feature-level fusion for recognition using CASIA and IITD databases. PSO and BSA are used for feature selection.…”

Section: Fig 2 Fusion Levels In Multimodal Biometric Systemsmentioning

confidence: 99%

Experimental Analysis of Face and Iris Biometric Traits Based on the Fusion Approach

Kumari,

Nagapushpa,

Jayalaxmi

et al. 2023

IJST

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Objectives : To develop an efficient algorithm for face and iris multimodal traits on ORL and CASIA dataset and to increase the performance rate and decrease the error rate of the model. The main goal is to increase the performance rate and decrease the error rate of the model. Methods: The proposed algorithm utilizes a fusion of face and iris modalities using Stationary Wavelet Transform (SWT) and Local Binary Pattern (LBP) techniques. The Principal Component Analysis (PCA) is applied to reduce the dimensionality of each sample, improving efficiency while preserving the most relevant information. The relevant characteristics from both face and iris modalities are fused to create a comprehensive pattern for an individual. Findings: The obtained features are compared with the features of the database images using a Euclidean Distance classifier. The performance of the proposed model is evaluated using the ORL and CASIA iris datasets. The accuracy achieved by the proposed algorithm is 99.42%, demonstrating robustness. Novelty: The algorithm introduces feature-level fusion, combining the characteristics of both face and iris modalities. The model encompasses the training and recognition phases within a biometric system. During the training phase, the biometric modality is captured and processed using the fusion of SWT+LBP+PCA techniques to form a template for each user. These templates are later stored in the database for recognition purposes.

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A Proposed Biometric Technique for Improving Iris Recognition

Cited by 9 publications

References 18 publications

Facial Recognition Technology-Based Attendance Management System Application in Smart Classroom

Facial Recognition Technology-Based Attendance Management System Application in Smart Classroom

Improved Human Identification by Multi-biometric Image Sensor Integration With a Deep Learning Approach

Experimental Analysis of Face and Iris Biometric Traits Based on the Fusion Approach

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