Fusion of Face and Iris Biometrics

Connaughton, Ryan; Bowyer, Kevin W.; Flynn, Patrick J.

doi:10.1007/978-1-4471-4402-1_12

Cited by 32 publications

(16 citation statements)

References 7 publications

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“…A feature level fusion using face and iris was addressed by Huo et al [28] in 2015, in this paper author extract the feature sets from face and iris through a two-dimensional Gabor filter bank and finally the identification is accomplished through a fusion strategy of PCA and SVM. In 2016, Connaughton et al [16] proposed a match score fusion based face and iris multimodal system where the features of face and iris are captured through a single sensor. For data acquisition Iris on the Move (IOM) sensor was selected where to handle a large range of subject heights the presence of multiple cameras are used.…”

Section: International Journal Of Innovative Technology and Exploringmentioning

confidence: 99%

A Novel Technique to Enhance Performance of Multibiometric Framework using Bin based Classifier Based on Multi-algorithm Score Level Fusion

Modak*,

Jha

2020

IJITEE

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This work shows a multibiometric framework to upgrade the recognition rate and reduce the error rate using Bin based classifier based on score level (multi-algorithm) fusion. In this work Bin based classifier is used as the combination rule which integrate the matching scores from two distinct modalities namely iris and face. An optimization technique, PSO is utilized to minimize the unwanted information after combination of the feature sets of the iris and face using different feature extraction algorithms like PCA, LDA and LBP. The test results demonstrate that the multibiometric system as a Bin based classifier employing multi-algorithm score level fusion provides better outcomes than the other fusion rule like Likelihood Ratio based fusion, Linear Discriminant Analysis (LDA) and support vector machine (SVM). The experimental result on Face (ORL, BANCA,FERET) and iris (CASIA, UBIRIS) shows that the proposed multimodal system derived from CBBC (continuous bin based classifier) with PSO as an optimization technique achieve EER=0.012 , which outperform than the other fusion technique with EER=0.018 for SVM (RBF) and EER=0.02 for SVM linear.

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Section: International Journal Of Innovative Technology and Exploringmentioning

confidence: 99%

A Novel Technique to Enhance Performance of Multibiometric Framework using Bin based Classifier Based on Multi-algorithm Score Level Fusion

Modak*,

Jha

2020

IJITEE

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“…In the proposed system, different fusion approaches are applied to the proposed ensemblebased framework to achieve a higher level of generalization, and robustness [14]. Fusion techniques in such systems can be described as: (a) feature-level that aims to combine all the features extracted among patches into one feature vector in the feature space, (b) score-level attempts to combine the scores generated among patches using multiple classifiers trained per each patch, (c) feature-level concatenates several representations (descriptors), (d) score-level fusion of representations within the ensemble to provide the final score, and finally (e) decision-level of descriptors to produce the final response after applying decision thresholds as represented in Figure 4.…”

Section: Operational Phasementioning

confidence: 99%

Robust watch-list screening using dynamic ensembles of SVMs based on multiple face representations

Bashbaghi

Granger

Sabourin

et al. 2017

Machine Vision and Applications

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Abstract. Still-to-video face recognition (FR) is an important function in video surveillance, where faces captured over a network of video cameras are matched against reference stills of target individuals. Screening faces against a watch-list is a challenging video surveillance application because the appearance of faces vary due to changing capture conditions and operational domains. The facial models used for matching may not be representative of faces captured with video cameras because they are typically designed a priori with only one reference still. In this paper, a multi-classifier framework is proposed for robust still-to-video FR based on multiple and diverse face representations of a single reference face still. During enrollment of a target individual, the single reference face still is modeled using an ensemble of SVM classifiers based on different patches and face descriptors. Multiple feature extraction techniques are applied to patches isolated in the reference still to generate a diverse SVM pool that provides robustness to common nuisance factors (e.g., variations in illumination and pose). The estimation of discriminant feature subsets, classifier parameters, decision thresholds, and ensemble fusion functions is achieved using the high-quality reference still and a large number of faces captured in lower quality video of non-target individuals in the scene. During operations, the most competent subset of SVMs are dynamically selected according to capture conditions. Finally, a head-face tracker gradually regroups faces captured from different people appearing in a scene, while each individual-specific ensemble performs face matching. The accumulation of matching scores per face track leads to a robust spatio-temporal FR when accumulated ensemble scores surpass a detection threshold. Experimental results obtained with the Chokepoint and COX-S2V datasets show a significant improvement in performance w.r.t. reference systems, especially when individual-specific ensembles (1) are designed using exemplar-SVMs rather than one-class SVMs, and (2) exploit score-level fusion of local SVMs (trained using features extracted from each patch), rather than using either decision-level or feature-level fusion with a global SVM (trained by concatenating features extracted from patches).

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“…There are a lot of examples of multimodal biometric fusions. The most popular are fusions of physiological modalities like face and iris [4,5] or fingerprint and iris [6,7]. There are also works that present a fusion of the same modality measured by different sensors [8].…”

Section: Information Fusion In Biometricsmentioning

confidence: 99%

Fusion of eye movement and mouse dynamics for reliable behavioral biometrics

Kasprowski

Harężlak

2016

Pattern Anal Applic

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This paper presents the first attempt to fuse two different kinds of behavioral biometrics: mouse dynamics and eye movement biometrics. Mouse dynamics were collected without any special equipment, while an affordable The Eye Tribe eye tracker was used to gather eye movement data at a frequency of 30 Hz, which is also potentially possible using a common web camera. We showed that a fusion of these techniques is quite natural and it is easy to prepare an experiment that collects both traits simultaneously. Moreover, the fusion of information from both signals gave 6.8 % equal error rate and 92.9 % accuracy for relatively short registration time (20 s on average). Achieving such results were possible using dissimilarity matrices based on dynamic time warping distance.

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Fusion of Face and Iris Biometrics

Cited by 32 publications

References 7 publications

A Novel Technique to Enhance Performance of Multibiometric Framework using Bin based Classifier Based on Multi-algorithm Score Level Fusion

A Novel Technique to Enhance Performance of Multibiometric Framework using Bin based Classifier Based on Multi-algorithm Score Level Fusion

Robust watch-list screening using dynamic ensembles of SVMs based on multiple face representations

Fusion of eye movement and mouse dynamics for reliable behavioral biometrics

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