• International Journal of Fuzzy Logic and Intelligent Systems
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• v.7 no.2
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• pp.148-153
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• 2007

This paper deals with implementing a steganography based multi-modal biometric system. For this purpose, we construct a multi-biometrics system based on the face and iris recognition. Here, the feature vector of iris pattern is hidden in the face image. The recognition system is designed by the fuzzy-based Linear Discriminant Analysis(LDA), which is an expanded approach of the LDA method combined by the theory of fuzzy sets. Furthermore, we present a watermarking method that can embed iris information into face images. Finally, we show the advantages of the proposed watermarking scheme by computing the ROC curves and make some comparisons recognition rates of watermarked face images with those of original ones. From various experiments, we found that our proposed scheme could be used for establishing efficient and secure multi-modal biometric systems.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.7 no.1
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• pp.71-76
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• 2007

This paper deals with implementing a steganography based multi-modal biometric system. For this purpose, we construct a multi-biometrics system based on the face and iris recognition. Here, the feature vector of iris pattern is hidden in the face image. The recognition system is designed by the fuzzy-based Linear Discriminant Analysis(LDA), which is an expanded approach of the LDA method combined by the theory of fuzzy sets. Furthermore, we present a watermarking method that can embed iris information into face images. Finally, we show the advantages of the proposed watermarking scheme by computing the ROC curves and make some comparisons recognition rates of watermarked face images with those of original ones. From various experiments, we found that our proposed scheme could be used for establishing efficient and secure multi-modal biometric systems.

• The KIPS Transactions:PartB
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• v.11B no.7 s.96
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• pp.885-892
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• 2004

In this paper, we propose a multi-modal biometrics system based on face and signature recognition system. Here, the face recognition system is designed by fuzzy LDA, and the signature recognition system is implemented with the LDA and segment matching methods. To effectively aggregate two systems, we obtain statistical distribution models based on matching values for genuine and impostor, respectively. And then, the final verification is Performed by the support vector machine. From the various experiments, we find that the proposed method shows high recognition rates comparing with the conventional methods.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.8
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• pp.1456-1462
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• 2008

In general, it is known a single biometric-based personal authentication has limitation to improve recognition rate due to weakness of individual recognition scheme. The recognition rate of face recognition system can be reduced by environmental factor such as illumination, while speaker verification system does not perform well with added surrounding noise. In this paper, a multi-modal biometric system composed of face and voice recognition system is proposed in order to improve the performance of the individual authentication system. The proposed empirical weight sum rule based on the reliability of the individual authentication system is applied to improve the performance of multi-modal biometrics. Since the proposed system is implemented using JAVA applet with security function, it can be utilized in the field of user authentication on the generic Web.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2005.11a
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• pp.299-301
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• 2005

본 연구는 생체정보를 이용하여 개인을 인증하고 확인하기 위한 방법으로 기존 단일 생체인식 기법의 단점을 보완하기 위해 홍채와 얼굴을 이용한 다중생체인식(Multi-Modal Biometrics Recognition)기법을 연구하였다. 중국 홍채 데이터베이스 CASIA(Chinese Academy of Science)에 Gabor Wavelet과 FLDA(Fuzzy Linear Discriminant Analysis)를 사용하여 특징벡터를 획득하였으며, FERET(FERET(Face Recognition Technology) 얼굴영상데이터를 사용하여 FERET 연구에서 매우 우수한 성능을 보인 EBGM알고리듬으로 특징벡터를 획득하였다. 이로부터 얻어진 두 score 값에 대하여 다양한 균등화 과정을 시도해 보았으며, 등록자와 침입자를 구분하기 위한 Fusion Algorithm으로 Bayesian Classifier, Support vector machine, Fisher's linear discriminant를 사용하였다. 또한, 널리 사용되는 방법 중 Weighted Summation을 이용하여 다중생체인식의 성능을 비교해 보았다.

• Proceedings of the Korea Information Processing Society Conference
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• 2005.11a
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• pp.427-430
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• 2005

본 연구는 기존 단일 생체인식의 단점을 보완하기 위해 다중생체인식(Multi-Modal Biometrics Recognition)기법을 연구한 것으로, 홍채영상을 이용한 홍채인식과 얼굴영상을 이용한 얼굴인식을 융합하기 위해 다양한 방법을 시도해 보았다. 이에, CBNU 홍채 영상데이터를 사용한 홍채인식은 Gabor Wavelet과 FLDA(Fuzzy Linear Discriminant Analysis)를 이용하였으며, FERET 얼굴영상데이터를 사용한 얼굴인식도 FLDA를 이용하여 패턴의 특징을 추출하고 matching에 따른 score를 각각 획득한다. 얻어진 두 score 값에 대하여 다양한 균등화과정을 사용해 보았으며, 다중생체인식 융합방법중 하나인 Weight sum rule을 적용하여 인식률을 얻었다. 또한, 단일 생체인식의 경우보다 좋은 성능을 나타냄을 확인하기 위해 FRR과 FAR등의 인식률 평가방법을 사용하였으며, 기존 단일생체인식 방법보다 좋은 성능을 보이고 있음을 확인할 수 있었다.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.7 no.2
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• pp.200-205
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• 2006

Abstract This paper presents a personal identification method based on BMME and LDA for images acquired at anterior and posterior occlusion expression of teeth. The method consists of teeth region extraction, BMME, and pattern recognition forthe images acquired at the anterior and posterior occlusion state of teeth. Two occlusions can provide consistent teeth appearance in images and BMME can reduce matching error in pattern recognition. Using teeth images can be beneficial in recognition because teeth, rigid objects, cannot be deformed at the moment of image acquisition. In the experiments, the algorithm was successful in teeth recognition for personal identification for 20 people, which encouraged our method to be able to contribute to multi-modal authentication systems.

• Proceedings of the IEEK Conference
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• summer
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• pp.435-437
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• 2004

This paper presents a personal identification method using teeth images. The method uses images for teeth expressions of anterior and posterior occlusion state and LDA-based technique. Teeth images give merits for recognition because teeth, rigid objects, cannot be deformed at the moment of image acquisition. In the experiments, personal identification for 12 people was successful. It was shown that our method can contribute to multi-modal authentication systems.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.1
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• pp.49-54
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• 2006

The recent fingerprint recognition system has unstable factors, such as copy of fingerprint patterns and hacking of fingerprint feature point, which mali cause significant system error. Thus, in this research, we used the fingerprint as the main recognition device and then implemented the multi-biometric recognition system in serial using the speech recognition which has been widely used recently. As a multi-biometric recognition system, once the speech is successfully recognized, the fingerprint recognition process is run. In addition, speaker-dependent DTW(Dynamic Time Warping) algorithm is used among existing speech recognition algorithms (VQ, DTW, HMM, NN) for effective real-time process while KSOM (Kohonen Self-Organizing feature Map) algorithm, which is the artificial intelligence method, is applied for the fingerprint recognition system because of its calculation amount. The experiment of multi-biometric recognition system implemented in this research showed 2 to $7\%$ lower FRR (False Rejection Ratio) than single recognition systems using each fingerprints or voice, but zero FAR (False Acceptance Ratio), which is the most important factor in the recognition system. Moreover, there is almost no difference in the recognition time(average 1.5 seconds) comparing with other existing single biometric recognition systems; therefore, it is proved that the multi-biometric recognition system implemented is more efficient security system than single recognition systems based on various experiments.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.44 no.4 s.316
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• pp.36-44
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• 2007

In this paper, we implement the speech & face recognition system to support various ubiquitous sensor network application services such as switch control, authentication, etc. using wireless audio and image interface. The proposed system is consist of the H/W with audio and image sensor and S/W such as speech recognition algorithm using psychoacoustic model, face recognition algorithm using PCA (Principal Components Analysis) and LDPC (Low Density Parity Check). The proposed speech and face recognition systems are inserted in a HOST PC to use the sensor energy effectively. And improve the accuracy of speech and face recognition, we implement a FEC (Forward Error Correction) system Also, we optimized the simulation coefficient and test environment to effectively remove the wireless channel noises and correcting wireless channel errors. As a result, when the distance that between audio sensor and the source of voice is less then 1.5m FAR and FRR are 0.126% and 7.5% respectively. The face recognition algorithm step is limited 2 times, GAR and FAR are 98.5% and 0.036%.