• Journal of Information Processing Systems
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• v.10 no.4
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• pp.555-567
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• 2014

Biometric performance improvement is a challenging task. In this paper, a hierarchical strategy fusion based on multimodal biometric system is presented. This strategy relies on a combination of several biometric traits using a multi-level biometric fusion hierarchy. The multi-level biometric fusion includes a pre-classification fusion with optimal feature selection and a post-classification fusion that is based on the similarity of the maximum of matching scores. The proposed solution enhances biometric recognition performances based on suitable feature selection and reduction, such as principal component analysis (PCA) and linear discriminant analysis (LDA), as much as not all of the feature vectors components support the performance improvement degree.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.45 no.5
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• pp.162-172
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• 2008

In this paper, we propose a multimodal speaker verification method using teeth image and voice as biometric trait for personal verification in mobile terminal equipment. The proposed method obtains the biometric traits using image and sound input devices of smart-phone that is one of mobile terminal equipments, and performs verification with biometric traits. In addition, the proposed method consists the multimodal-fashion of combining two biometric authentication scores for totally performance enhancement, the fusion method is accompanied a weighted-summation method which has comparative simple structure and superior performance for considering limited resources of system. The performance evaluation of proposed multimodal speaker authentication system conducts using a database acquired in smart-phone for 40 subjects. The experimental result shows 8.59% of EER in case of teeth verification 11.73% in case of voice verification and the multimodal speaker authentication result presented the 4.05% of EER. In the experimental result, we obtain the enhanced performance more than each using teeth and voice by using the simple weight-summation method in the multimodal speaker verification system.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.1
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• pp.132-141
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• 2015

There might have been weakness in securing user authentication or verification with real time service approach, while existing unimodal biometric authentication has been used mainly for user identification and recognition. Accordingly, it is essential to research and develop ways that upgrade security performance with multi biometric based real time authentication and verification technology. This paper focused to suggest binding assignment and strategy for developing multi biometric authentication technology through investigation of advanced study and patents. Description includes introduction, technology outline, technology trend, patent analysis, and conclusion.

• Journal of Korea Multimedia Society
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• v.20 no.2
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• pp.170-178
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• 2017

Biometric recognition is one of the major challenging topics which needs high performance of recognition accuracy. Most of existing methods rely on a single source of biometric to achieve recognition. The recognition accuracy in biometrics is affected by the variability of effects, including illumination and appearance variations. In this paper, we propose a new multimodal biometrics recognition using convolutional neural network. We focus on multimodal biometrics from face and periocular regions. Through experiments, we have demonstrated that facial multimodal biometrics features deep learning framework is helpful for achieving high recognition performance.

• Proceedings of the IEEK Conference
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• 2006.06a
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• pp.875-876
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• 2006

In this paper, we propose a multimodal biometric embedded system. It is designed to support face, iris, fingerprint and vascular pattern recognition. We use a S3C2440A based on ARM926T core processor that is made in Samsung. The system has support various external device interfaces for multi biometric sensors, and RFID/Smart Card reader/writer. Additionally, it has a 6" LCD panel and numeric keypad for easy GUI. The embedded system offers useful environments to develop better biometric algorithms for stand alone biometric system and accelerator hardware modules for real time operation.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.15 no.4
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• pp.93-100
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• 2005

In this paper, we describe biometric watermarking techniques for secure user verification on the remote, multimodal biometric system employing both fingerprint and face information, and compare their effects on verification accuracy quantitatively. To hide biometric data with watermarking techniques, we first consider possible two scenarios. In the scenario 1, we use a fingerprint image as a cover work and hide facial features into it. On the contrary, we hide fingerprint features into a facial image in the Scenario 2. Based on the experimental results, we confirm that the Scenario 2 is superior to the Scenario 1 in terms of the verification accuracy of the watermarking image.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.5 no.2
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• pp.157-163
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• 2005

In this paper, we present the multimodal system based on the fusion of two user-friendly biometric modalities: Iris and Face. In order to reach robust identification and verification we are going to combine two different biometric features. we specifically apply 2-D discrete wavelet transform to extract the feature sets of low dimensionality from iris and face. And then to obtain Reduced Joint Feature Vector(RJFV) from these feature sets, Direct Linear Discriminant Analysis (DLDA) is used in our multimodal system. In addition, the Synergetic Neural Network(SNN) is used to obtain matching score of the preprocessed data. This system can operate in two modes: to identify a particular person or to verify a person's claimed identity. Our results for both cases show that the proposed method leads to a reliable person authentication system.

• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.4
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• pp.310-316
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• 2013

Biometric techniques have been widely used for various areas including criminal identification due to their reliability. However, they have some drawbacks when the biometric information is divulged to illegal users. This paper proposed multimodal biometric system using a real fuzzy vault by RN-ECC for protecting fingerprint and face template. This proposed method has some advantages to regenerate a key value compared with face or fingerprint based verification system having non-regenerative nature and to implement advanced biometric verification system by fusion of both fingerprint and face recognition. From the various experiments, we found that the proposed method shows high recognition rates comparing with the conventional methods.

• Journal of Digital Convergence
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• v.13 no.6
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• pp.151-156
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• 2015

In this paper, we have proposed personal multimodal biometric authentication system based on face detection, recognition and speaker verification for smart-phone environment. Proposed system detect the face with Modified Census Transform algorithm then find the eye position in the face by using gabor filter and k-means algorithm. Perform preprocessing on the detected face and eye position, then we recognize with Linear Discriminant Analysis algorithm. Afterward in speaker verification process, we extract the feature from the end point of the speech data and Mel Frequency Cepstral Coefficient. We verified the speaker through Dynamic Time Warping algorithm because the speech feature changes in real-time. The proposed multimodal biometric system is to fuse the face and speech feature (to optimize the internal operation by integer representation) for smart-phone based real-time face detection, recognition and speaker verification. As mentioned the multimodal biometric system could form the reliable system by estimating the reasonable performance.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.20 no.5
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• pp.133-138
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• 2010

We present a new multimodal biometric authentication method, which performs both feature-level fusion and decision-level fusion. After generating support vector machines for new features made by integrating face and voice features, the final decision for authentication is made by integrating decisions of face SVM classifier, voice SVM classifier and integrated features SVM clssifier. We justify our proposal by comparing our method with traditional one by experiments with XM2VTS multimodal database. The experiments show that our multilevel fusion algorithm gives higher recognition rate than the existing schemes.