Session variability modelling for face authentication

McCool, Chris; Wallace, Roy; McLaren, Mitchell; Shafey, Laurent El; Marcel, Sébastien

doi:10.1049/iet-bmt.2012.0059

Cited by 35 publications

(46 citation statements)

References 30 publications

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“…GMMs have formed the basis of state-of-the-art speaker authentication systems for over a decade [11,9] and it was recently shown that incorporating session variability modelling into a GMM system produces state-of-the-art results for face authentication [12]. Also, the combination of GMM-based systems that use session variability modelling produces a state-of-the-art bi-modal (face and speaker) authentication system [8].…”

Section: Face and Speaker Authentication Systemsmentioning

confidence: 99%

“…In this work, we consider two approaches to session variability modelling, inter-session variability (ISV) modelling [22] and total variability (TV) modelling [9]. Both methods were initially proposed for speaker authentication [22,9] before being applied to face authentication [12,10]. In the remainder of this section, we first describe the GMM baseline system, followed by the more advanced ISV and TV techniques.…”

Section: Gmm-based Modellingmentioning

confidence: 99%

“…(4), known as linear scoring [24], has been adopted in the speaker authentication literature and has also been applied to face authentication [16]. It relies on the mean centralised first order sufficient statistics of the UBM given the observations O t , as given in Equation (9) of [12].…”

Section: Gaussian Mixture Modellingmentioning

confidence: 99%

“…Inter-session variability (ISV) modelling [22] and joint factor analysis (JFA) [27] are two session variability modelling techniques, used in the context of a GMM-based system, that have been successfully applied to both speaker [22,28,26] and face authentication [4,12]. Both techniques aim to estimate session variation like audio or image noise in order to compensate for it.…”

Section: Inter-session Variability Modellingmentioning

confidence: 99%

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Bi-modal biometric authentication on mobile phones in challenging conditions

Khoury

Shafey

McCool

et al. 2014

Image and Vision Computing

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This paper examines the issue of face, speaker and bi-modal authentication in mobile environments when there is significant condition mismatch. We introduce this mismatch by enrolling client models on high quality biometric samples obtained on a laptop computer and authenticating them on lower quality biometric samples acquired with a mobile phone. To perform these experiments we develop three novel authentication protocols for the large publicly available MOBIO database. We evaluate state-of-the-art face, speaker and bi-modal authentication techniques and show that inter-session variability modelling using Gaussian mixture models provides a consistently robust system for face, speaker and bi-modal authentication. It is also shown that multi-algorithm fusion provides a consistent performance improvement for face, speaker and bi-modal authentication. Using this bi-modal multi-algorithm system we derive a state-of-the-art authentication system that obtains a half total error rate of 6.3% and 1.9% for Female and Male trials, respectively.

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Section: Face and Speaker Authentication Systemsmentioning

confidence: 99%

Section: Gmm-based Modellingmentioning

confidence: 99%

Section: Gaussian Mixture Modellingmentioning

confidence: 99%

Section: Inter-session Variability Modellingmentioning

confidence: 99%

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Bi-modal biometric authentication on mobile phones in challenging conditions

Khoury

Shafey

McCool

et al. 2014

Image and Vision Computing

Self Cite

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“…The feature extraction parameters for the GMM/UBM and for the LBP-based classifier were fixed, according to reference values taken from other related works [76,90,91], and they are shown in the Table 4-5 …”

Section: (Hereinafter Lbp-based Classifier)mentioning

confidence: 99%

An Evaluation of Bimodal Recognition Systems Based on Voice and Facial Images

MACIÁ¹

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Face Recognition in Challenging Environments: An Experimental and Reproducible Research Survey

Günther

Shafey

Marcel

2016

Face Recognition Across the Imaging Spectrum

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One important type of biometric authentication is face recognition, a research area of high popularity with a wide spectrum of approaches that have been proposed in the last few decades. The majority of existing approaches are conceived for or evaluated on constrained still images. However, more recently research interests have shifted towards unconstrained "in-the-wild" still images and videos. To some extent, current state-of-the-art systems are able to cope with variability due to pose, illumination, expression, and size, which represent the challenges in unconstrained face recognition. To date, only few attempts have addressed the problem of face recognition in mobile environment, where high degradation is present during both data acquisition and transmission. This book chapter deals with face recognition in mobile and other challenging environments, where both still images and video sequences are examined. We provide an experimental study of one commercial of-the-shelf and four recent open-source face recognition algorithms, including color-based linear discriminant analysis, local Gabor binary pattern histogram sequences, Gabor grid graphs and inter-session variability modeling. Experiments are performed on several freely available challenging still image and video face databases, including one mobile database, always following the evaluation protocols that are attached to the databases. Finally, we supply an easily extensible opensource toolbox to re-run all the experiments, which includes the modeling techniques, the evaluation protocols and metrics used in the experiments, and provides a detailed description on how to re-generate the results.

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Session variability modelling for face authentication

Cited by 35 publications

References 30 publications

Bi-modal biometric authentication on mobile phones in challenging conditions

Bi-modal biometric authentication on mobile phones in challenging conditions

An Evaluation of Bimodal Recognition Systems Based on Voice and Facial Images

Face Recognition in Challenging Environments: An Experimental and Reproducible Research Survey

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