Fusion of Near Infrared Face and Iris Biometrics

Zhang, Zhijian; Wang, Rui; Pan, Ke; Li, Stan Z.; Pei-ren, Zhang

doi:10.1007/978-3-540-74549-5_19

Cited by 42 publications

(10 citation statements)

References 9 publications

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“…For classification purposes, WPNN classifier is used where features of face and iris are feed to make the final decision of whether the claimed user is truly accepted or rejected. A score level fusion of near infrared face and iris was addressed by Zhang et al [18] in 2007 the proposed framework was tested using different combination rule and it is found that on a close set the genuine accept rate (GAR) of face and two irises increase from 97.35% to 99.75% and on an open set it is increased from 83.31% to 98.12% when FAR is at 0.001.In 2008, Morizet et al [24] present a novel approach for face and iris based on a precise statistical analysis of bootstrapped match scores generated from similarity matrices. The overall performance of the biometric system depends on the separation between genuine and imposter distribution, to boost the exhibition of the framework the separation must be expanded.…”

Section: Related Workmentioning

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: Related Workmentioning

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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“…There are no free available multimodal databases which combine face and iris modalities of the same person (real-user). However, in most of the recent fusion studies [10,22,23,27] on face and iris biometrics, experiments are carried out on independent face and iris databases which result in the creation of chimeric users (the virtual subjects created with biometric traits of different users) [38]. To validate the performance of algorithms and fusion methods in our multimodal biometric system, a multimodal biometric database using the ORL face database [39] and the CASIA iris database [40] is constructed.…”

Section: A Databasementioning

confidence: 99%

A Robust Multi-Biometric System with Compact Code for Iris and Face

Nouar¹,

Taleb²

2018

IJEEI

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Multimodal biometric can overcome the limitations of the single biometric trait and gives better classification accuracy. This paper proposes a face-iris multimodal biometric system based on fusion at the matching score level. The iris recognition system is composed of segmentation, normalization, feature encoding and matching. The wavelet transform is used in feature extraction to generate a compact feature vector length of 128 bits; this reduces the computational time and storage of the iris code. The face recognition system is composed of enhancement, feature encoding and matching. The new method called 'Phase-based Gabor Fisher Classifier (PBGFC)' is used in feature extraction; PBGFC employs only 16 Gabor filters, i.e., filters with 2 scales and eight orientations. This fact makes the resulting feature vector for the PBGFC method very compact. The scores from iris and face are then combined using several score normalization and fusion techniques. To validate our approach, experiments are conducted on the iris and face images obtained from the CASIA and ORL datasets respectively. The results show that our multimodal biometric system achieves higher accuracy than both single biometric approaches and the other existing multi-biometric systems based on fusion of iris and face.

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“…It has been investigated in many existing works and it was proved that there is a substantial impact on the overall system performance by considering the performance metrics of the fusion level. In [3] the signal level fusion operation is performed to combine the iris and face images and the scores are assigned to formulate the composite images and finally normalized using a min-max rule to optimize the modality. In [6] Log-Gabor transformations driven feature level coding for template creations and particle swarm optimization (PSO) is used for feature selection to minimize the redundant information during template creation.…”

Section: Related Workmentioning

confidence: 99%

Texture Driven Hierarchical Fusion for Multi-Biometric Sys-tem

Rachapalli¹,

Kalluri²

2018

IJET

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This article presents hierarchical fusion models for multi-biometric systems with improved recognition rate. Isolated texture regions are used to encode spatial variations from the composite biometric image which is generated by signal level fusion scheme. In this paper, the prominent issues of the existing multi-biometric system, namely, fusion methodology, storage complexity, reliability and template security are discussed. Here wavelet decomposition driven multi-resolution approach is used to generate the composite images. Texture feature metrics are extracted from multi-level texture regions and principal component analyzes are evaluated to select potentially useful texture values during template creation. Here through consistency and correlation driven hierarchical feature selection both inter-class similarity and intra-class variance problems can be solved. Finally, t-normalized feature level fusion is incorporated as a last stage to create the most reliable template for the identification process. To ensure the security and add robustness to spoof attacks random key driven permutations are used to encrypt the generated multi-biometric templates before storing it in a database. Our experimental results proved that the proposed hierarchical fusion and feature selection approach can embed fine detailed information about the input multi modal biometric images with the least complex identification process.

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Fusion of Near Infrared Face and Iris Biometrics

Cited by 42 publications

References 9 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

A Robust Multi-Biometric System with Compact Code for Iris and Face

Texture Driven Hierarchical Fusion for Multi-Biometric Sys-tem

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