Application of Computer Vision for Multi-Layered Security to ATM Machine using Deep Learning Concept

Kalmani, Shailaja; Dilna, U.

doi:10.1109/icaaic53929.2022.9793149

Cited by 3 publications

(1 citation statement)

References 17 publications

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“…Fingerprint, retina, iris recognition, speaker recognition, hand and finger geometry, vein geometry, voice identity, etc., [2], [3] are just a few standard authentication methods. Due to the myriad facial traits and the ease with which facial recognition systems can be implemented, face recognition has emerged as the most significant physiological biometric modality to manage data security [4][5][6][7][8]. These systems are deceived by spoofing attacks such as images, movies, cut photos, masks, etc.…”

Section: Introductionmentioning

confidence: 99%

Empirical Performance Analysis of Deep Convolutional Neural Networks Architectures for Face Liveness Detection

Khairnar,

Gite,

Thepade

2024

Preprint

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In the realm of biometrics, face recognition (F.R.) is one of the most exciting new developments. In the past decade, computer vision and artificial intelligence advancements have improved face recognition systems by several orders of magnitude. Many attacks can be launched against these systems, such as the low-cost and low-effort Presentation attacks. Face liveness detection is gaining momentum in research. With the advent of deep learning, observing the performance of pre-trained DCNN Architectures for Face Liveness Detection will be interesting. The paper proposes an Empirical Performance analysis of eight pre-trained DCNN Architectures allies VGG16, VGG19, ResNet50, InceptionResNetV2, MobileNetV2, DenseNet201, InceptionV3, Xception. The experimentation carried out on the NUAA dataset (120000 images) & and the Replay attack dataset has shown that the better performance is given by DenseNet201, closely followed by MobileNetV2. This study offers significant contributions to the understanding of the dynamic nature of face recognition technologies.

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Section: Introductionmentioning

confidence: 99%

Empirical Performance Analysis of Deep Convolutional Neural Networks Architectures for Face Liveness Detection

Khairnar,

Gite,

Thepade

2024

Preprint

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An Effective Cardless Atm Transaction Using Computer Vision Techniques

Priya¹,

Jeeva²,

Pradeep³

et al. 2023

2023 9th International Conference on Advanced Computing and Communication Systems (ICACCS)

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Face Liveness Detection Using Artificial Intelligence Techniques: A Systematic Literature Review and Future Directions

Khairnar

Gite

Kotecha

et al. 2023

BDCC

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Biometrics has been evolving as an exciting yet challenging area in the last decade. Though face recognition is one of the most promising biometrics techniques, it is vulnerable to spoofing threats. Many researchers focus on face liveness detection to protect biometric authentication systems from spoofing attacks with printed photos, video replays, etc. As a result, it is critical to investigate the current research concerning face liveness detection, to address whether recent advancements can give solutions to mitigate the rising challenges. This research performed a systematic review using the PRISMA approach by exploring the most relevant electronic databases. The article selection process follows preset inclusion and exclusion criteria. The conceptual analysis examines the data retrieved from the selected papers. To the author, this is one of the foremost systematic literature reviews dedicated to face-liveness detection that evaluates existing academic material published in the last decade. The research discusses face spoofing attacks, various feature extraction strategies, and Artificial Intelligence approaches in face liveness detection. Artificial intelligence-based methods, including Machine Learning and Deep Learning algorithms used for face liveness detection, have been discussed in the research. New research areas such as Explainable Artificial Intelligence, Federated Learning, Transfer learning, and Meta-Learning in face liveness detection, are also considered. A list of datasets, evaluation metrics, challenges, and future directions are discussed. Despite the recent and substantial achievements in this field, the challenges make the research in face liveness detection fascinating.

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Application of Computer Vision for Multi-Layered Security to ATM Machine using Deep Learning Concept

Cited by 3 publications

References 17 publications

Empirical Performance Analysis of Deep Convolutional Neural Networks Architectures for Face Liveness Detection

Empirical Performance Analysis of Deep Convolutional Neural Networks Architectures for Face Liveness Detection

An Effective Cardless Atm Transaction Using Computer Vision Techniques

Face Liveness Detection Using Artificial Intelligence Techniques: A Systematic Literature Review and Future Directions

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