Multi-instance cancelable iris authentication system using triplet loss for deep learning models

Sandhya, Mulagala; Morampudi, Mahesh Kumar; Pruthweraaj, Indragante; Garepally, Pranay Sai

doi:10.1007/s00371-022-02429-x

Cited by 15 publications

(7 citation statements)

References 38 publications

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“…The bio-convolution is performed on the original feature data to transform them into another version noninvertibly. Sandhya et al [91] proposed a multi-instance cancellable iris system using a CNN trained with triple loss for feature extraction. Both random projection and random cross-folding are employed to achieve irreversibility.…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

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Feature extraction and learning approaches for cancellable biometrics: A survey

Yang,

Wang,

et al. 2024

CAAI Trans on Intel Tech

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Biometric recognition is a widely used technology for user authentication. In the application of this technology, biometric security and recognition accuracy are two important issues that should be considered. In terms of biometric security, cancellable biometrics is an effective technique for protecting biometric data. Regarding recognition accuracy, feature representation plays a significant role in the performance and reliability of cancellable biometric systems. How to design good feature representations for cancellable biometrics is a challenging topic that has attracted a great deal of attention from the computer vision community, especially from researchers of cancellable biometrics. Feature extraction and learning in cancellable biometrics is to find suitable feature representations with a view to achieving satisfactory recognition performance, while the privacy of biometric data is protected. This survey informs the progress, trend and challenges of feature extraction and learning for cancellable biometrics, thus shedding light on the latest developments and future research of this area.

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Section: Convolutional Neural Networkmentioning

confidence: 99%

“…Sandhya et al. [91] proposed a multi‐instance cancellable iris system using a CNN trained with triple loss for feature extraction. Both random projection and random cross‐folding are employed to achieve irreversibility.…”

Section: Feature Extraction and Learning Approachesmentioning

confidence: 99%

Feature extraction and learning approaches for cancellable biometrics: A survey

Yang,

Wang,

et al. 2024

CAAI Trans on Intel Tech

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“…The proposed framework based on Deep Neural Networks (DNN) was tested on 50 subjects only. The author in [51] proposed a Multi-Instance Cancelable iris authentication Deep Learning (MICBTDL) and used a CNN (triplet loss) and trained to differentiate a positive image from a negative on IITD and MMU iris dataset images. Both [52] and [53] proposed to encrypt the Iris Codes using classical cryptography algorithms.…”

Section: Authentication-based Cancellable Biometrics Approachesmentioning

confidence: 99%

Current Multi-factor of Authentication: Approaches, Requirements, Attacks and Challenges

Mohammed¹,

Dziyauddin²,

Latiff³

2023

IJACSA

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Now-a-days, with the rapid and broad emergence of local or remote access to services on the internet. Authentication represents an important security control requirement and the MFA is recommended to mitigate the weaknesses in the SFA. MFA techniques can be classified into two main approaches: based biometric and non-biometric approaches. However, there is a problem to maintain the tradeoff between security and accuracy. The studies that have been reviewed on both authentication mechanisms are found contradictory in the direction of others. In the direction of authentication-based biometrics the researchers tended to increase the recognition accuracy, while in the other direction, the researchers proposed to combine many authentication factors to increase the security layers. The main contribution of this survey is to review and spotlight on the current state of the arts in both authentication mechanisms to achieve a secure user identity. This paper provides a review of authentication protocols and security requirements. In addition to a detailed review with a comparison of secure one-time passcode generation and distribution. Furthermore, a comprehensive review of cancelable biometrics techniques, attacks, and requirements. Finally, providing a summary of key challenges and future research directions.

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“…[18] uses pre-trained AlexNet to extract features and SVM to perform classification. [31] proposes a Multi-Instance Cancelable Iris System (MICBTDL). MICBTDL uses the operational triple loss of AlexNet training for feature extraction and stores the feature vectors as cancelable templates.…”

Section: Deep Learning Featuresmentioning

confidence: 99%

“…The difference in the adequate number of available iris pixels can be used to dynamically enhance the periocular information obtained from the iris images. [31] uses MLP as the matching module. Experiments were conducted on the iris datasets of IITD and MMU to verify the effectiveness of the framework of MICBTDL.…”

Section: Multi-layer Perceptronsmentioning

confidence: 99%

Deep Learning for Iris Recognition: A Review

Yin¹,

He²,

Zhang³

et al. 2023

Preprint

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Iris recognition is a secure biometric technology known for its stability and privacy. With no two irises being identical and little change throughout a person's lifetime, iris recognition is considered more reliable and less susceptible to external factors than other biometric recognition methods. Unlike traditional machine learning-based iris recognition methods, deep learning technology does not rely on feature engineering and boasts excellent performance. This paper collects 120 relevant papers to summarize the development of iris recognition based on deep learning. We first introduce the background of iris recognition and the motivation and contribution of this survey. Then, we present the common datasets widely used in iris recognition. After that, we summarize the key tasks involved in the process of iris recognition based on deep learning technology, including identification, segmentation, presentation attack detection, and localization. Finally, we discuss the challenges and potential development of iris recognition. This review provides a comprehensive sight of the research of iris recognition based on deep learning.

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Multi-instance cancelable iris authentication system using triplet loss for deep learning models

Cited by 15 publications

References 38 publications

Feature extraction and learning approaches for cancellable biometrics: A survey

Feature extraction and learning approaches for cancellable biometrics: A survey

Current Multi-factor of Authentication: Approaches, Requirements, Attacks and Challenges

Deep Learning for Iris Recognition: A Review

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