Designing Efficient Spoof Detection Scheme for Face Biometric

Eskandari, Maryam; Sharifi, Omid

doi:10.1007/978-3-319-94211-7_46

Cited by 2 publications

(6 citation statements)

References 22 publications

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“…The anti-spoofing methods for face biometric has been studied recently using different handcrafted and deep learnt techniques [4,7,[14][15][16][22][23][24][25][26][27][28][29]. In [27] multi-level local binary (MLBP) and CNN schemes has been proposed to obtain hybrid features from two different exploited feature vectors.…”

Section: Related Workmentioning

confidence: 99%

“…In general, spoof detection is quite challenging in the biometric area and accordingly the investigation of its effect on different modalities such as face, iris, fingerprint, multimodal biometric systems, etc. is encouraged [2][3][4][5][6][7][8][9][10][11][12][13]. Typically, texture, motion and liveness analyses are common techniques in biometric systems to counter spoofing attacks [2,[14][15][16].…”

Section: Introductionmentioning

confidence: 99%

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Face Anti-Spoofing Scheme Using Handcraft Based and Deep Learning Methods

Sharifi

2020

Çukurova Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi

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Malicious parties which impersonate systems by fake identities affect recognition performance of biometric systems. This study focuses on a strength anti-spoofing scheme based on decision level fusion to monitor individuals in term of real and fake. The proposed fake detection scheme involves consideration of both handcrafted and deep learned techniques on face images to differentiate real and fake individuals. In this context, convolutional neural network (CNN) and Log-Gabor filter methods are used to learn deep representations and extract facial features of images respectively. In order to improve the robustness of proposed anti-spoofing framework, fusion of Log-Gabor and CNN methods is considered by applying decision-level-fusion technique. Finally, the performance of proposed antispoofing scheme is examined on public spoof databases such as Print-Attack and Replay-Attack face databases to detect fake facial images.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Face Anti-Spoofing Scheme Using Handcraft Based and Deep Learning Methods

Sharifi

2020

Çukurova Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi

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“…The problem of spoof attack detection for face biometric has been studied recently using different handcrafted and deep learnt techniques [12,16,[23][24][25][31][32][33][34][35][36][37][38]. In [16], a novel double anti-spoofing pipeline has been proposed for face biometrics based on selection of optimized textures and image quality assessment techniques for print and video attacks. The paper applied different texture and image quality algorithms to compare the ability of their proposed framework.…”

Section: Related Workmentioning

confidence: 99%

“…The feature extraction step of this study considers an extension of LBP texture extractor called OVLBP in order to describe the local spatial structure of face images. In general, LBP introduced by Ojala In general, the use of histogram-based methods for face spoof scenarios to detect attacks specifically print and video attacks has been considered as sufficient factor in several studies [16,18,[32][33][34]. This study applies idea of overlapped LBP histograms to obtain more significant histograms as handcrafted facial texture extractor for print and video attacks.…”

Section: Handcrafted Facial Texture Extraction Using Ovlbpmentioning

confidence: 99%

“…Spoof detection in biometrics is quite challenging and therefore has encouraged the biometric society to investigate the effect of this kind of counterfeit events on different modalities such as face, iris, fingerprint, multimodal biometric systems, etc. [11][12][13][14][15][16][17][18][19][20][21][22]. As abovementioned, texture, motion and liveness analyses can be considered in biometric systems to counter spoofing attacks [14,[23][24][25].…”

Section: Introductionmentioning

confidence: 99%

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Score-Level-based Face Anti-Spoofing System Using Handcrafted and Deep Learned Characteristics

Sharifi¹

2019

IJIGSP

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Recognition performance of biometric systems is affected through spoofing attacks made by fake identities. The focus of this paper is on presenting a new scheme based on score level and decision level fusion to monitor individuals in term of real and fake. The proposed fake detection scheme involve consideration of both handcrafted and deep learned techniques on face images to differentiate real and fake individuals. In this approach, convolutional neural network (CNN) and overlapped histograms of local binary patterns (OVLBP) methods is used to extract facial features of images. The produced matching scores provided by CNN and OVLBP then combined to form a fused score vector. Finally, the last decision on real and attack images is done by combining decisions of hybrid scheme using majority vote of CNN, OVLBP and their fused vector. Experimental results on public spoof databases such as Print-Attack and Replay-Attack face databases demonstrate the strength of the proposed anti-spoofing method for fake detection.

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Designing Efficient Spoof Detection Scheme for Face Biometric

Cited by 2 publications

References 22 publications

Face Anti-Spoofing Scheme Using Handcraft Based and Deep Learning Methods

Face Anti-Spoofing Scheme Using Handcraft Based and Deep Learning Methods

Score-Level-based Face Anti-Spoofing System Using Handcrafted and Deep Learned Characteristics

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