Journal Article•
Fusion of face and iris features for multimodal biometrics
TL;DR: This paper combines face and iris features for developing a multimodal biometrics approach, which is able to diminish the drawback of single biometric approach as well as to improve the performance of authentication system.
Abstract: The recognition accuracy of a single biometric authentication system is often much reduced due to the environment, user mode and physiological defects. In this paper, we combine face and iris features for developing a multimode biometric approach, which is able to diminish the drawback of single biometric approach as well as to improve the performance of authentication system. We combine a face database ORL and iris database CASIA to construct a multimodal biometric experimental database with which we validate the proposed approach and evaluate the multimodal biometrics performance. The experimental results reveal the multimodal biometrics verification is much more reliable and precise than single biometric approach.
Citations
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TL;DR: This paper presents a method that combines face and iris biometric traits with the weighted score level fusion technique to flexibly fuse the matching scores from these two modalities based on their weight availability.
Abstract: The iris and face are among the most promising biometric traits that can accurately identify a person because their unique textures can be swiftly extracted during the recognition process. However, unimodal biometrics have limited usage since no single biometric is sufficiently robust and accurate in real-world applications. Iris and face biometric authentication often deals with non-ideal scenarios such as off-angles, reflections, expression changes, variations in posing, or blurred images. These limitations imposed by unimodal biometrics can be overcome by incorporating multimodal biometrics. Therefore, this paper presents a method that combines face and iris biometric traits with the weighted score level fusion technique to flexibly fuse the matching scores from these two modalities based on their weight availability. The dataset use for the experiment is self established dataset named Universiti Teknologi Malaysia Iris and Face Multimodal Datasets (UTMIFM), UBIRIS version 2.0 (UBIRIS v.2) and ORL face databases. The proposed framework achieve high accuracy, and had a high decidability index which significantly separate the distance between intra and inter distance.
104 citations
Cites background from "Fusion of face and iris features fo..."
...(2007), Chen and Chu (2005) F b b Face and speech Sanderson and Paliwal (2004) S...
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...…(2005), Ross and Govindarajan (2005) T s fe Score level fusion Face and iris Eskandari et al. (2013), YunHong et al. (2003), Lee et al. (2007), Chen and Chu (2005) F b b Face and speech Sanderson and Paliwal (2004) S Fingerprints and palmprints Slobodan et al. (2008) E Decision level fusion…...
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...Lee et al. (2007), Chen and Chu (2005) and Eskandari, Toygar, and Demirel (2013) also presents the score level fusion based on face and iris biometrics using the classification approach....
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TL;DR: The results show that the proposed feature selection method is able to improve the classification accuracy in terms of total error rate and the support vector machine-based fusion method also gave very promising results.
Abstract: Multimodal biometric can overcome the limitation possessed by single biometric trait and give better classification accuracy. This paper proposes face-iris multimodal biometric system based on fusion at matching score level using support vector machine (SVM). The performances of face and iris recognition can be enhanced using a proposed feature selection method to select an optimal subset of features. Besides, a simple computation speed-up method is proposed for SVM. The results show that the proposed feature selection method is able improve the classification accuracy in terms of total error rate. The support vector machine-based fusion method also gave very promising results.
75 citations
TL;DR: Experimental results show that the performance of the proposed method can bring obvious improvement comparing to the unimodal biometric identification methods and the previous fused face-iris methods.
Abstract: Fusion of multiple biometrics for human authentication performance improvement has received considerable attention. This paper presents a novel multimodal biometric authentication method integrating face and iris based on score level fusion. For score level fusion, support vector machine (SVM) based fusion rule is applied to combine two matching scores, respectively from Laplacianface based face verifier and phase information based iris verifier, to generate a single scalar score which is used to make the final decision. Experimental results show that the performance of the proposed method can bring obvious improvement comparing to the unimodal biometric identification methods and the previous fused face-iris methods.
55 citations
Cites methods from "Fusion of face and iris features fo..."
...Some integration schemes about fusion of face and iris have been proposed in the previous literatures [5,6]....
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...In this sec− tion, we make a comparison with Wang’s method [5] and Chen’s method [6], the details about which has been de− scribed in the introduction section....
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...In previous studies, sev− eral integration schemes about fusion of face and iris have been developed [5,6]....
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01 Jan 2013
TL;DR: This chapter presents a system which simultaneously acquires face and iris samples using a single sensor, with the goal of improving recognition accuracy while minimizing sensor cost and acquisition time.
Abstract: This chapter presents a system which simultaneously acquires face and iris samples using a single sensor, with the goal of improving recognition accuracy while minimizing sensor cost and acquisition time. The resulting system improves recognition rates beyond the observed recognition rates for either isolated biometrics.
51 citations
Cites background from "Fusion of face and iris features fo..."
...Chen and Te Chu use an unwei ghted average of the outputs of matchers based on neural networks [4]....
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TL;DR: Improved recognition accuracies are achieved compared to the individual systems and multimodal systems using other local or global feature extractors for both modalities.
Abstract: Fusion of multiple biometrics combines the strengths of unimodal biometrics to achieve improved recognition accuracy. In this study, face and iris biometrics are used to obtain a robust recognition system by using several feature extractors, score normalization and fusion techniques. Global and local feature extractors are used to extract face and iris features separately, and then, the fusion of these modalities is performed on different subsets of face and iris image databases of ORL, FERET, CASIA and UBIRIS. The proposed method uses Local Binary Patterns local feature extractor and subspace Linear Discriminant Analysis global feature extractor on face and iris images, respectively. Face and iris scores are normalized using tanh normalization, and then, Weighted Sum Rule is applied for the fusion of these two modalities. Improved recognition accuracies are achieved compared to the individual systems and multimodal systems using other local or global feature extractors for both modalities.
43 citations
Cites background from "Fusion of face and iris features fo..."
...Since face and iris biometrics are independent from each other, in this study, an arbitrary but fixed iris class is assigned to a face class using different face and iris databases as in [7,8]....
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...Vast studies around face and iris fusion strategies have been developed [7,8] in the past few years....
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References
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06 Aug 2002
TL;DR: A concept for the optimization of nonlinear functions using particle swarm methodology is introduced, and the evolution of several paradigms is outlined, and an implementation of one of the paradigm is discussed.
Abstract: A concept for the optimization of nonlinear functions using particle swarm methodology is introduced. The evolution of several paradigms is outlined, and an implementation of one of the paradigms is discussed. Benchmark testing of the paradigm is described, and applications, including nonlinear function optimization and neural network training, are proposed. The relationships between particle swarm optimization and both artificial life and genetic algorithms are described.
35,104 citations
10 Dec 2002
TL;DR: Algorithms developed by the author for recognizing persons by their iris patterns have now been tested in many field and laboratory trials, producing no false matches in several million comparison tests.
Abstract: The principle that underlies the recognition of persons by their iris patterns is the failure of a test of statistical independence on texture phase structure as encoded by multiscale quadrature wavelets. The combinatorial complexity of this phase information across different persons spans about 249 degrees of freedom and generates a discrimination entropy of about 3.2 bits/mm/sup 2/ over the iris, enabling real-time decisions about personal identity with extremely high confidence. Algorithms first described by the author in 1993 have now been tested in several independent field trials and are becoming widely licensed. This presentation reviews how the algorithms work and presents the results of 9.1 million comparisons among different eye images acquired in trials in Britain, the USA, Korea, and Japan.
2,437 citations
Additional excerpts
...In the multimodal biometric system, we select the face and iris features for constructing a high reliable biometric system, because the face recognition is friendly and non-invasive whereas iris recognition is the most accurate biometrics to date among all biometrics systems [14]....
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TL;DR: This paper addresses the problem of information fusion in biometric verification systems by combining information at the matching score level by combining three biometric modalities (face, fingerprint and hand geometry).
Abstract: User verification systems that use a single biometric indicator often have to contend with noisy sensor data, restricted degrees of freedom, non-universality of the biometric trait and unacceptable error rates Attempting to improve the performance of individual matchers in such situations may not prove to be effective because of these inherent problems Multibiometric systems seek to alleviate some of these drawbacks by providing multiple evidences of the same identity These systems help achieve an increase in performance that may not be possible using a single biometric indicator Further, multibiometric systems provide anti-spoofing measures by making it difficult for an intruder to spoof multiple biometric traits simultaneously However, an effective fusion scheme is necessary to combine the information presented by multiple domain experts This paper addresses the problem of information fusion in biometric verification systems by combining information at the matching score level Experimental results on combining three biometric modalities (face, fingerprint and hand geometry) are presented
1,611 citations
TL;DR: This paper addresses the problem of information fusion in verification systems and experimental results on combining three biometric modalities (face, fingerprint and hand geometry) are presented.
Abstract: User verification systems that use a single biometric indicator often have to contend with noisy sensor data, restricted degrees of freedom and unacceptable error rates. Attempting to improve the performance of individual matchers in such situations may not prove to be effective because of these inherent problems. Multimodal biometric systems seek to alleviate some of these drawbacks by providing multiple evidences of the same identity. These systems also help achieve an increase in performance that may not be possible by using a single biometric indicator. This paper addresses the problem of information fusion in verification systems. Experimental results on combining three biometric modalities (face, fingerprint and hand geometry) are also presented.
790 citations
"Fusion of face and iris features fo..." refers background in this paper
...Many multimodal biometrics methods and strategies have been proposed [2-8]....
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TL;DR: This article goes into detail about the BioID system functions, explaining the data acquisition and preprocessing techniques for voice, facial, and lip imagery data and the classification principles used for optical features and the sensor fusion options.
Abstract: Biometric identification systems, which use physical features to check a person's identity, ensure much greater security than password and number systems. Biometric features such as the face or a fingerprint can be stored on a microchip in a credit card, for example. A single feature, however, sometimes fails to be exact enough for identification. Another disadvantage of using only one feature is that the chosen feature is not always readable. Dialog Communication Systems (DCS AG) developed BioID, a multimodal identification system that uses three different features-face, voice, and lip movement-to identify people. With its three modalities, BioID achieves much greater accuracy than single-feature systems. Even if one modality is somehow disturbed-for example, if a noisy environment drowns out the voice-the ether two modalities still lead to an accurate identification. This article goes into detail about the system functions, explaining the data acquisition and preprocessing techniques for voice, facial, and lip imagery data. The authors also explain the classification principles used for optical features and the sensor fusion options (the combinations of the three results-face, voice, lip movement-to obtain varying levels of security).
386 citations