Face–iris multimodal biometric scheme based on feature level fusion

Huo, Guang; Liu, Yuanning; Zhu, Xiaodong; Dong, Hongxing; He, Fei

doi:10.1117/1.jei.24.6.063020

Cited by 22 publications

(11 citation statements)

References 28 publications

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“…Huo et al [16] established a Multi-modal system of feature level face-iris .2D Gabor filter bank used to extract the features of both the modalities, these features are converted using histogram statistics. The fusion recognition depends on support vector machine and principal components analysis.…”

Section: Review Criteriamentioning

confidence: 99%

Comparison of levels and fusion approaches for multimodal biometrics

S¹,

Reddy²

2021

IJEECS

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The biometric-based authentication system occupies maximal space in the field of security administration. Biometric applications are swiftly accelerating in day-to-day life such as computer login, smart homes, online banking, hospitals, border areas, industries, forensics, e-voting attendance system and investigation of crime. A reliable and accurate recognition body can be achieved with multimodal biometric methodologies. In this paper, we discuss starting with an introduction to biometric systems followed by their classification, and advantages as well as disadvantages. In today’s world, most of the systems are unimodal biometrics having a lot of limitations to overcome those multimodal biometrics comes in to picture. In this paper we have discussed comprehensive representation on the system of multimodal biometric, various modes of undertakings, the significance of information fusion, a different section is allotted on the various possible levels of fusion involving sensor-level, feature-level, score-level, and decision -level as well as different rules of fusion.

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Section: Review Criteriamentioning

confidence: 99%

Comparison of levels and fusion approaches for multimodal biometrics

S¹,

Reddy²

2021

IJEECS

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“…An accuracy rate of 98.75% is achieved using the proposed method based on ORL face and CASIA iris datasets which are shown improvement compared to unimodal framework. 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.…”

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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“…They reported recognition rate of 99.12 and 99.7% in (ORL face and iris database) and (IIS face and iris database), respectively. Huo et al in [12] developed face-iris multi-modal system based on featurelevel fusion. They used 2D Gabor filter bank for features extraction, these features are transformed by histogram statistics into an energy-orientation variance histogram.…”

Section: Related Workmentioning

confidence: 99%

“…Huo et al . in [12] developed face–iris multi‐modal system based on feature‐level fusion. They used 2D Gabor filter bank for features extraction, these features are transformed by histogram statistics into an energy‐orientation variance histogram.…”

Section: Related Workmentioning

confidence: 99%

Face–iris multi‐modal biometric system using multi‐resolution Log‐Gabor filter with spectral regression kernel discriminant analysis

2018

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A multi-modal biometric system is used to verify or identify a person by exploiting information of more than one biometric modality. It combines the strengths of the unimodal biometric system to solve their limitations. This study proposes schemes of multi-modal biometric system based on texture information extracted from face and two iris (left and right) using hybrid level of fusion. Feature extraction is the key step to get a robust recognition system. Multi-resolution two-dimensional Log-Gabor filter combined with spectral regression kernel discriminant analysis is exploited to extract features from both face and iris modalities. These features are used in the fusion and the classification process. The proposed schemes were tested using CASIA Iris Distance database in the verification mode. The experimental results show that the proposed multi-modal biometric system yields attractive performances of up to 0.24% in terms of equal error rate and outperforms the recent similar existing state-of-the-art methods.

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Face–iris multimodal biometric scheme based on feature level fusion

Cited by 22 publications

References 28 publications

Comparison of levels and fusion approaches for multimodal biometrics

Comparison of levels and fusion approaches for multimodal biometrics

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

Face–iris multi‐modal biometric system using multi‐resolution Log‐Gabor filter with spectral regression kernel discriminant analysis

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