Fingerprint Liveness Detection From Single Image Using Low-Level Features and Shape Analysis

Dubey, Rohit K.; Goh, Jonathan; Thing, Vrizlynn L. L.

doi:10.1109/tifs.2016.2535899

Cited by 72 publications

(13 citation statements)

References 39 publications

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“…For a specific spoofing technique, a special anti-spoofing system is designed that cannot be used globally. Dubey et al [17] proposed a method of combining multiple techniques for feature extraction such as the SURF method for the detection of the local point of interest, the pyramid multi-scale characteristic of the Histogram of Oriented Gradients (HOG), and the Gabor texture characteristic. They combined all the characteristics to identify the spoof and separate it from genuine subjects.…”

Section: Fingerprintmentioning

confidence: 99%

Auditory Perception Based Anti-Spoofing System for Human Age Verification

et al. 2019

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Biometric systems are considered an efficient component for identification in the developing modern technologies. The aim of biometric systems is to verify or determine the identity of a user through his/her biological and behavioral characteristics. The threat of spoof attacks is always an important issue in biometric verification and authentication, which requires an updated and stronger protection system. In this article, we propose an anti-spoofing system based on auditory perception responses. To the best of our knowledge, this is the first time that an auditory perception based anti-spoofing system has been presented for age verification. The proposed auditory perception based anti-spoofing system was evaluated with 770 trials conducted by many subjects of each gender and age range (12–65 years of age). The results achieved are encouraging, as the auditory perception based system showed the lowest Equal Error Rate (EER) value of 5.5%.

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Section: Fingerprintmentioning

confidence: 99%

Auditory Perception Based Anti-Spoofing System for Human Age Verification

et al. 2019

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“…Dubey et al [26] fuse several low-level features including the gradient features from speeded-up robust features (SURF), pyramid histogram of oriented gradients (PHOG) and also the texture features of Gabor wavelet using dynamic score level integration. Besides that, the authors also combined with shape analysis to produce a good result.…”

Section: The Fake Fingerprint Detection Software Approachesmentioning

confidence: 99%

An investigation of fake fingerprint detection approaches

Ahmad¹,

Hassan²,

Othman³

2017

AIP Conference Proceedings

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“…When signing into a banking application utilizing facial acknowledgment, for instance, a functioning liveness discovery framework may expect clients to squint while it checks their face. [3] Inactive liveness recognition, in the interim, may filter a client's face to guarantee that a genuine human face is available with the person's appropriate profundity shapes. Numerous liveness discovery techniques, particularly dynamic methodologies, take more time to recognize clients, diminishing the speed, accommodation, and straightforwardness of biometric ID.…”

Section: Introductionmentioning

confidence: 99%

Face Spoofing Detection using Mixed Feature with Deep Convolutional Neural Networks

N V*,

U.,

2020

IJRTE

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Nowadays, face biometric-based access control systems are becoming ubiquitous in daily life while they are still vulnerable to spoofing attacks. Developing robust and reliable methods to prevent such frauds is unavoidable. As deep learning techniques have achieved satisfactory performances in computer vision, they have also been applied to face spoofing detection. However, the numerous parameters in these deep learning-based detection methods cannot be updated to optimum due to limited data. In this paper,a highly accurate face spoof detection system using multiple features and deep learning is proposed. The input video is broken into frames using content-based frame extraction. From each frame, the face of the person is cropped.From the cropped images multiple features like Histogram of Gradients (HoG), Local Binary Pattern (LBP), Center Symmetric LBP (CSLBP), and Gray level co-occurrence Matrix (GLCM) are extracted to train the Convolutional Neural Network(CNN). Training and testing are performed separately by using collected sample data.Experiments on the standard spoof database called Replay-Attack database the proposed system outperform other state-of-the-art techniques, presenting great results in terms of attack detection.

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Fingerprint Liveness Detection From Single Image Using Low-Level Features and Shape Analysis

Cited by 72 publications

References 39 publications

Auditory Perception Based Anti-Spoofing System for Human Age Verification

Auditory Perception Based Anti-Spoofing System for Human Age Verification

An investigation of fake fingerprint detection approaches

Face Spoofing Detection using Mixed Feature with Deep Convolutional Neural Networks

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