DeBNet: Multilayer Deep Network for Liveness Detection in Face Recognition System

Garg, Shilpa; Mittal, Sumit; Kumar, Pardeep; Athavale, Vijay Anant

doi:10.1109/spin48934.2020.9070853

Cited by 13 publications

(5 citation statements)

References 31 publications

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“…Facial recognition is an alternative physical biometric. Systems with facial recognition have up to 96.74% accuracy [7], 99.79% detection rate [8], and 0.0116% EER [53]. These impressive results indicate the effectiveness of facial biometrics.…”

Section: Related Workmentioning

confidence: 99%

“…Biometric recognition is a promising approach and has been accepted in academia and widely used in the industry. For example, many mobile devices now support user authorization with various biometric modalities, such as fingerprint scanning and face, voice, or iris recognition [3][4][5][6][7][8][9][10][11][12][13]. These modalities are examples of physiological biometrics; that is, user identification is based on the distinctive physical characteristics of a user obtained by using specialized hardware sensors.…”

Section: Introductionmentioning

confidence: 99%

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Framework for user behavioural biometric identification using a multimodal scheme with keystroke trajectory feature and recurrent neural network on a mobile platform

Tse¹,

Hung²

2022

IET Biometrics

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Diverse applications are used on mobile devices. Because of the increasing dependence on information systems, immense amounts of personal and sensitive data are stored on mobile devices. Thus, security or privacy breaches are a major challenge. To protect mobile systems and the private information on these systems from being accessed by adversaries, a framework for mobile user identification through the use of a multimodal behavioural biometrics scheme with a keystroke trajectory feature is presented herein. Conventionally, mobile devices have been protected by mechanisms such as PINs or passwords. However, these approaches have numerous disadvantages. Therefore, approaches that employ keystroke biometrics for secure and reliable mobile device identification have been proposed. Because unimodal behavioural biometrics identification mechanisms have limited accuracy and effectiveness, a multimodal scheme that includes different behavioural biometric traits, such as keystroke and swipe biometric traits, is examined. However, the information provided by the spatial and temporal features of keystroke biometrics is not comprehensive. Therefore, a trajectory model is derived to describe the behavioural biometric uniqueness of a user. In the user identification phase, a multistream recurrent neural network (RNN) is adopted. The results reveal that the proposed trajectory model performs well, and the multimodal scheme using an RNN with a late fusion method provides accurate identification results. The proposed system achieved an accuracy of 95.29%, F1 score of 94.64%, and equal error rate of 1.78%. Thus, the proposed mobile identification system is capable of resisting attacks that standard mechanisms may be vulnerable to and represents a valuable contribution to cyber security.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Framework for user behavioural biometric identification using a multimodal scheme with keystroke trajectory feature and recurrent neural network on a mobile platform

Tse¹,

Hung²

2022

IET Biometrics

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“…The results of experiments demonstrated that the introduced mechanism had generated optimal outcomes as compared to other schemes. Shilpa Garg, et.al (2020) formulated a robust and effectual method named DeBNet with the objective of detecting face liveness [16]. The multilayer deep network was exploited to attain effective outcomes for diverse percentage of training set of images.…”

Section: Literature Review a Face Spoof Detection Using Deep Learningmentioning

confidence: 99%

Hybrid Algorithm for Face Spoof Detection

Mittal¹,

Kaur²,

Oberoi³

2022

IJRASET

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The face spoof detection is the approach which can detect spoofed face. The face spoof detection methods has various phases which include pre-processing, feature extraction and classification. The classification algorithm can classify into two classes which are spoofed or not spoofed. The KNN approach is used previously with the GLCM algorithm for the face spoof detection which give low accuracy. In this research work, the hybrid classification method is proposed which is the combination of random forest, k nearest neighbour and SVM Classifiers. The simulation outcomes depict that the introduced method performs more efficiently in comparison with the conventional techniques with regard to accuracy. Keywords: Face Spoof, KNN, Hybrid Classifier, GLCM

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“…Nanthini [5] propose a novel deep CNN based Liveness Detection network (LDnet) using the combination of both 2D CNN and 3D CNN. Garg et al [6] . use a multilayer deep network DBN for robust and accurate liveness detection in face recognition for varying percentages of training images.…”

Section: Introductionmentioning

confidence: 99%

Lightweight face liveness detection method based on 32×24 thermal imaging images and ShuffleNet

Zhang,

Xie,

et al. 2023

Third International Conference on Computer Vision and Pattern Analysis (ICCPA 2023)

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This paper aims to solve the problem of deploying face liveness detection methods on mobile terminals with low computing resources. A lightweight face liveness detection method based on 32 × 24 thermal imaging images and ShuffleNet is proposed. This method can be deployed on mobile devices with low computing resources on the premise of ensuring detection accuracy. In addition, this paper also proposes an optimization strategy, which can completely eliminate the situation that the fake face is misidentified as a real face, and at the same time control the probability of the real face misidentified as a fake face at a very low level. The experimental results show that the proposed method has high practical value.

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DeBNet: Multilayer Deep Network for Liveness Detection in Face Recognition System

Cited by 13 publications

References 31 publications

Framework for user behavioural biometric identification using a multimodal scheme with keystroke trajectory feature and recurrent neural network on a mobile platform

Framework for user behavioural biometric identification using a multimodal scheme with keystroke trajectory feature and recurrent neural network on a mobile platform

Hybrid Algorithm for Face Spoof Detection

Lightweight face liveness detection method based on 32×24 thermal imaging images and ShuffleNet

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