Face Spoofing Detection Using Local Graph Structure

Bashier, Housam Khalifa; Lau, Siong Hoe; Pang, Ying Han; Ping, Liew Yee; Li, Chiang Mee

doi:10.2991/ccit-14.2014.70

Cited by 6 publications

(3 citation statements)

References 14 publications

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“…Repudiation makes it possible for an individual to deny transactions he actually performed, claiming that they are the results of attacks. Future research efforts should consider the provision of antispoofing (or liveness detection) capabilities [113,114] in biometric key-based cryptosystems. Integrating liveness detection in a biometric authentication system will prevent spoofing attack and repudiation.…”

Section: Future Research Directionsmentioning

confidence: 99%

State of the Art in Biometric Key Binding and Key Generation Schemes

Jegede

Udzir

Abdullah

et al. 2022

Int. j. commun. netw. inf. secur.

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Direct storage of biometric templates in databases exposes the authentication system and legitimate users to numerous security and privacy challenges. Biometric cryptosystems or template protection schemes are used to overcome the security and privacy challenges associated with the use of biometrics as a means of authentication. This paper presents a review of previous works in biometric key binding and key generation schemes. The review focuses on key binding techniques such as biometric encryption, fuzzy commitment scheme, fuzzy vault and shielding function. Two categories of key generation schemes considered are private template and quantization schemes. The paper also discusses the modes of operations, strengths and weaknesses of various kinds of key-based template protection schemes. The goal is to provide the reader with a clear understanding of the current and emerging trends in key-based biometric cryptosystems.

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Section: Future Research Directionsmentioning

confidence: 99%

State of the Art in Biometric Key Binding and Key Generation Schemes

Jegede

Udzir

Abdullah

et al. 2022

Int. j. commun. netw. inf. secur.

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“…An evaluation of several IQA techniques was done in [34].Several studies have tested the efficacy of dynamic texture analysis in liveness detection in [35][36][37][38][39][40][41][42][43]. Static texture analysis on the other hand have been explored through large number of published studies in our research period in [4,[44][45][46][47][48][49][50][51][52][53][54][55][56][57][58]].…”

Section: Texture Analysismentioning

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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“…The authors in [4], [42] proposed to study these differences in the frequency domain. Some authors proposed to analyze in spatial domain by using a local texture descriptors, such as Local Binary Pattern (LBP) [43]- [45], Local Graph Structure (LGS) [46], [47]. Different from the other authors who study only the gray image, [7] proposed to exploit the texture-information in color spaces.…”

Section: ) Static Approachesmentioning

confidence: 99%

Face Presentation Attack Detection Based on a Statistical Model of Image Noise

et al. 2019

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The vulnerability of most existing face recognition and authentication systems against face presentation attacks (a.k.a. face spoofing attacks) has been mentioned and studied in many works. This paper introduces a novel parametric approach for face PAD using a statistical model of image noise. In fact, facial images from a presentation attack contain specific textural information caused by the presentation process which makes them different from bona-fide images. The subtle difference between bona-fide and presentation attack images can be interpreted by the difference regarding noise statistics within the skin zone of the face. Our solution is casted in the hypothesis testing framework. A new database for face PAD containing face bona-fide images and images of high-quality presentation attacks has been also introduced. The performance of the proposed approach was proven in the mentioned database. Experimental results show that, in a controlled situation, our solution performs better than the other approaches in the literature.

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Face Spoofing Detection Using Local Graph Structure

Cited by 6 publications

References 14 publications

State of the Art in Biometric Key Binding and Key Generation Schemes

State of the Art in Biometric Key Binding and Key Generation Schemes

Insight on face liveness detection: A systematic literature review

Face Presentation Attack Detection Based on a Statistical Model of Image Noise

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