Anti-spoofing in face recognition with liveness detection using pupil tracking

Kıllıoğlu, Mehmet; Taşkıran, Murat; Kahraman, Nihan

doi:10.1109/sami.2017.7880281

Cited by 38 publications

(26 citation statements)

References 9 publications

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“…A range of processing methods has been proposed with a vast majority operating in the visible spectrum. Many algorithms use specifically tailored features, which may be heavily pertinent to the kind of attack being detected by focusing on the texture analysis [ 14 ], motion analysis [ 15 ], life sign indicators [ 16 ] and 3D properties [ 17 ], also with a variety of machine learning based methods. Detection of 3D facial masks has been addressed in several works.…”

Section: Related Workmentioning

confidence: 99%

A Study on Presentation Attack Detection in Thermal Infrared

Kowalski

2020

Sensors

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Face recognition systems face real challenges from various presentation attacks. New, more sophisticated methods of presentation attacks are becoming more difficult to detect using traditional face recognition systems. Thermal infrared imaging offers specific physical properties that may boost presentation attack detection capabilities. The aim of this paper is to present outcomes of investigations on the detection of various face presentation attacks in thermal infrared in various conditions including thermal heating of masks and various states of subjects. A thorough analysis of presentation attacks using printed and displayed facial photographs, 3D-printed, custom flexible 3D-latex and silicone masks is provided. The paper presents the intensity analysis of thermal energy distribution for specific facial landmarks during long-lasting experiments. Thermalization impact, as well as varying the subject’s state due to physical effort on presentation attack detection are investigated. A new thermal face spoofing dataset is introduced. Finally, a two-step deep learning-based method for the detection of presentation attacks is presented. Validation results of a set of deep learning methods across various presentation attack instruments are presented.

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Section: Related Workmentioning

confidence: 99%

A Study on Presentation Attack Detection in Thermal Infrared

Kowalski

2020

Sensors

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“…The execution of LFR-ELM perspective was known to be preferable within both the databases as per the juxtaposition constituted for the end. Killioglu, et.al [22] proposed a maiden elevated algorithm to take out the pupils from the eye region. An erratic flank is selected by the proposed spoofing algorithm once the umpteenth constant numbers of frames that include pupils were identified.…”

Section: Related Workmentioning

confidence: 99%

Spoofing Face Detection using LBP Descriptor and KNN Classifier in Image Processing

Sharma*¹,

Chauhan²

2020

IJRTE

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Image processing in today’s world used for performing operations on images by using a process of making positive suggestion of face which can be in a photo or video in already existing face database. Extraction of face attributes is done in face detection from photos and also from videos. When any unauthorized person tries to enter in authentication system by presenting fraud image and video is termed as spoofing attack. Biometrics is a technology which recognizes characteristics of human and is prone to spoof attacks. The detection of spoofed faces by recognizing and exploring the fake face and genuine face images is called face spoof detection. The DWT method is used to inspect the textual attribute occurring within the test images. There is a possibility that some unusual disruptions are available like geometric disruption and the artificial texture disruption. Eigen face technique is applicable for taking out attributes. Histogram for every feature or attributes is determined and employed a collation of essence to find out face spoof detection. To explore even if the image is actual and gag, already used approach Support Vector Machine is used. To make face spoof detection more accurate KNN classifier will take the place of the SVM classifier. The Contrast are construct to inspect the performance of the suggest algorithm and the existing algorithm in two parameters accuracy and time of execution. Detection of spoofed faces can be used for security purpose, preventing crime, access control system.

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“…The movement of a certain part in the face such as a lip movement or eye blinking can be an indicator of liveness, eye state and mouth state are two main signs to be taken into consideration in life sign detection algorithms which were established in [68][69][70][71][72] respectively. A utilization of both indicators to build a robust liveness detection system was also proposed by A.Singh et al (2014) in [73].…”

Section: Life Sign Indicatorsmentioning

confidence: 99%

Insight on face liveness detection: A systematic literature review

Raheem¹,

Ahmad²,

Adnan³

2019

IJECE

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<p>To review researcher’s attempts in response to the problem of spoofing and liveness detection, mapping the research overview from the literature survey into a suitable taxonomy, exploring the basic properties of the field, motivation of using liveness detection methods in face recognition, and Problems that may restrain the advantages. We presented a subjected search on face recognition with liveness detection and its synonyms in four main databases: Web of science, Science Direct, Scopus and IEEE Xplore. We believe that these databases are widely inclusive enough to cover the literature.<em> </em>The final number of articles considered is 65 articles. 4 of them where review and survey articles that described a general overview about liveness detection and anti-spoofing methods. Since 2012, and despite of leaving some areas unestablished and needs more attention, researchers tried to keep track of liveness detection in several ways. No matter what their category is, articles concentrated on challenges that faces the full utility of anti-spoofing methods and recommended some solutions to overcome these challenges. In this paper, different types of liveness detection and face anti-spoofing techniques are investigated to keep researchers updated with what is being developed in this field.</p>

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Anti-spoofing in face recognition with liveness detection using pupil tracking

Cited by 38 publications

References 9 publications

A Study on Presentation Attack Detection in Thermal Infrared

A Study on Presentation Attack Detection in Thermal Infrared

Spoofing Face Detection using LBP Descriptor and KNN Classifier in Image Processing

Insight on face liveness detection: A systematic literature review

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