A Real-Time Face Detection Method Based on Blink Detection

Qi, Xiaobo; Wu, Chengpei; Qi, Huang; Shi, Ying; Duan, Kaige; Wang, Xiaobin

doi:10.1109/access.2023.3257986

Cited by 13 publications

(3 citation statements)

References 15 publications

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“…Blinking eyelids can protect the biometric system from attack attempts in the form of fake static images containing the image of real users [6]. Many works focus on using the mere fact of closing the eyes as a method of introducing a secret authentication code into the system [7][8].…”

Section: Related Workmentioning

confidence: 99%

Siamese neural network based algorithm for user recognition by their eye blinking

Malinowski,

Saeed

2024

Preprint

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The article proposes a new method for user recognition based on their unique eyelid blinking pattern. Our research aimed to develop a method that is resistant to shoulder surfing and brute force attacks, while also not requiring complex recording devices. Most user authentication methods utilizing eyelid blinking patterns are vulnerable to pattern replication attacks. On the other hand, methods using EEG sometimes require the use of complicated equipment to record the blinking event. In our study, we utilized the publicly available mEBAL database. The temporal eyelid movement patterns extracted from the samples in the database are analyzed by a Siamese neural network. The achieved results of 98.20% accuracy and 0.11 EER unequivocally demonstrate the superiority of the proposed method over other methods using eyelid blinking for user authentication.

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Section: Related Workmentioning

confidence: 99%

Siamese neural network based algorithm for user recognition by their eye blinking

Malinowski,

Saeed

2024

Preprint

View full text Add to dashboard Cite

show abstract

“…Blinking eyelids can protect the biometric system from attack attempts in the form of fake static images containing the image of real users [9]. Many works focus on using the mere fact of closing the eyes as a method of introducing a secret authentication code into the system [10][11].…”

Section: Related Workmentioning

confidence: 99%

Real-time siamese neural network based algorithm for user recognition by their eye blinking

Malinowski,

Saeed

2024

Preprint

View full text Add to dashboard Cite

The article proposes a new method for user recognition based on a unique eyelid blinking pattern. Our research aimed to develop a user recognition method using eyelid blinking that is resistant to shoulder surfing and brute force attacks, while also not requiring complex recording devices. Most user authentication methods utilizing eyelid blinking patterns are vulnerable to pattern replication attacks. On the other hand, methods using EEG sometimes require the use of complicated equipment to record the blinking event. In our study, we utilized the publicly available mEBAL database. The temporal eyelid movement patterns extracted from the samples in the database are analyzed by a Siamese neural network. Our achieved results of 98.20% accuracy and 0.11 EER unequivocally demonstrate the superiority of the proposed method over other methods using eyelid blinking for user authentication.

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“…The objective of our system is to provide a face and fingerprint recognition based virtual ATM authentication system with a reduced false positive rate when recognizing new users. H. Qi et al describes in [1] about LBAS_Resnet50, a real-time face identification technique based on blink detection, to address the issues of lighting and expression variations during real-time face recognition. To increase the recognition process's tolerance to lighting, the model uses ResNet50 as the foundational network structure and feeds the texture information that the LBP method extracts into the base network.…”

Section: Introductionmentioning

confidence: 99%

An In Depth Survey on Biometric ATM Handling through Fingerprint and Face Recognition using Deep Learning

Ghuge,

Avhad,

Burkul

et al. 2023

IJRASET

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The achievement of greater ease and dependability has been made possible by India's technical advancements. The banking industry has also prospered, achieving notable improvements that have benefited the consumer. Automated Teller Machines (ATMs) have transformed transaction capabilities and decreased the likelihood of human error. Cash can always be dispensed and deposited via ATMs. This can be done with the bank-issued cards, which make integration considerably simpler. However, there has been a rise in card theft and fraudulent transactions, which compromises the dependability and security of ATMs. Consequently, rather than recognizing the card as is the case with the current model, a methodology that identifies the person during the transaction is required to increase the security and dependability of Automated Teller Machines. Implementing a biometric authentication system will be necessary to realize user identification through a virtual ATM strategy. In order to create a very reliable and secure virtual ATM, this method covers the usage of face recognition in addition to fingerprint recognition using live streaming, Channel Boosted Convolutional Neural Networks, and One Time Password implementation. The research directives to come will provide a detailed explanation of the strategy.

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A Real-Time Face Detection Method Based on Blink Detection

Cited by 13 publications

References 15 publications

Siamese neural network based algorithm for user recognition by their eye blinking

Siamese neural network based algorithm for user recognition by their eye blinking

Real-time siamese neural network based algorithm for user recognition by their eye blinking

An In Depth Survey on Biometric ATM Handling through Fingerprint and Face Recognition using Deep Learning

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