Attention Augmented Face Morph Detection

Aghdaie, Poorya; Soleymani, Sobhan; Nasrabadi, Nasser M.; Dawson, Jeremy

doi:10.1109/access.2023.3254539

Cited by 2 publications

(1 citation statement)

References 68 publications

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“…HSEB matches the typical human-salience entropy. In [7], this explains the order to detect single image morphing attacks, this research uses spatial and channel attention modules to emphasize discriminative regions. System specifically provided quantitative evidence of the three visual attention modules' effectiveness for the downstream job of morph identification in a binary classification environment.…”

Section: IImentioning

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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Section: IImentioning

confidence: 99%