Results 31 to 40 of about 5,266 (270)

An Ensemble Model for Face Liveness Detection

open access: yes, 2022
In this paper, we present a passive method to detect face presentation attack a.k.a face liveness detection using an ensemble deep learning technique. Face liveness detection is one of the key steps involved in user identity verification of customers during the online onboarding/transaction processes.
Shekhar, Shashank   +3 more
openaire   +2 more sources

An Overview of Face Liveness Detection

open access: yesInternational Journal on Information Theory, 2014
Face recognition is a widely used biometric approach. Face recognition technology has developed rapidly in recent years and it is more direct, user friendly and convenient compared to other methods. But face recognition systems are vulnerable to spoof attacks made by non-real faces. It is an easy way to spoof face recognition systems by facial pictures
Chakraborty, Saptarshi, Das, Dhrubajyoti
openaire   +2 more sources

Face liveness detection algorithm based on deep learning

open access: yesDianzi Jishu Yingyong, 2019
Identity authentication technology has developed greatly, and there have been various fraudulent means of forging legitimate user information. Aiming at this problem, this paper proposes a deep learning face detection algorithm to analyze the difference ...
Huang Haixin, Zhang Dong
doaj   +1 more source

Deep convolutional neural networks for face and iris presentation attack detection: Survey and case study

open access: yes, 2020
Biometric presentation attack detection is gaining increasing attention. Users of mobile devices find it more convenient to unlock their smart applications with finger, face or iris recognition instead of passwords.
El-Din, Yomna Safaa   +2 more
core   +1 more source

A Face Spoofing Detection Method Based on Domain Adaptation and Lossless Size Adaptation

open access: yesIEEE Access, 2020
In this paper, a face spoofing detection method called the Fully Convolutional Network with Domain Adaptation and Lossless Size Adaptation (FCN-DA-LSA) is proposed.
Wenyun Sun   +3 more
doaj   +1 more source

Biometric antispoofing methods: A survey in face recognition [PDF]

open access: yes, 2014
Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works ...
Fiérrez, Julián   +2 more
core   +2 more sources

Face Liveness Detection Based on Parallel CNN

open access: yesJournal of Physics: Conference Series, 2020
Abstract In this paper, we develop an effective framework based on deep learning for face liveness detection. Liveness detection is a great challenge in computer vision. Over the past decade, the interest of people in safety management has increased, and face recognition technology has gradually expanded into the commercial areas ...
Xin Li   +4 more
openaire   +1 more source

Evaluating software-based fingerprint liveness detection using Convolutional Networks and Local Binary Patterns

open access: yes, 2014
With the growing use of biometric authentication systems in the past years, spoof fingerprint detection has become increasingly important. In this work, we implement and evaluate two different feature extraction techniques for software-based fingerprint ...
Lotufo, Roberto de Alencar   +2 more
core   +1 more source

How far did we get in face spoofing detection?

open access: yes, 2018
The growing use of control access systems based on face recognition shed light over the need for even more accurate systems to detect face spoofing attacks.
Oliveira, Luciano   +3 more
core   +2 more sources

A Novel Deep Learning Architecture With Image Diffusion for Robust Face Presentation Attack Detection

open access: yesIEEE Access, 2023
Face presentation attack detection (PAD) is considered to be an essential and critical step in modern face recognition systems. Face PAD aims at exposing an imposter or an unauthorized person seeking to deceive the authentication system.
Madini O. Alassafi   +8 more
doaj   +1 more source

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