• The Journal of the Korea Contents Association
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• v.16 no.11
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• pp.11-19
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• 2016

The personal identification based on the biometrics has emerged as one of the new trend. This study attempted to explore and examine the user recognition in the use of the personal identification based on the biometrics in the respect of self-efficacy, trustiness, security, and safety alongside the effect of the recognition on the future use intention through survey. The result of this study demonstrated the effect on the use intention of the perceived trustiness and ease of the fingerprint identification, perceived ease of the iris identification and the perceived trustiness of the vein identification. The result of this study is expected to suggest direction on the application of the biometrics considering user recognition.

• 한국HCI학회:학술대회논문집
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• 2008.02c
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• pp.172-177
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• 2008

Biometrics is essential for person identification because of its uniqueness from each individuals. Face recognition technology has advantage over other biometrics because of its convenience and non-intrusive characteristics. In this paper, we will present a overview of face recognition technology including face detection, feature extraction, and face recognition system. For face detection, we will describe template based method and face component based approach. PCA and LDA approach will be discussed for feature extraction, and nearest neighbor classifiers -will be covered for matching. Large database and the standardized performance evaluation methodology is essential in order to support state-of-the-art face recognition system. Also, 3D based face recognition technology is the key solution for the pose, lighting and expression variations in many applications.

• Proceedings of the IEEK Conference
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• 2008.06a
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• pp.961-962
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• 2008

This paper proposes a touchless fingerprint acquisition device for five fingerprints. In conventional devices, they are focused for taking 1 touchless fingerprint image. But this device is taking 5 fingerprint image at a time. Also, it considers problems for focus and rolling in touchless fingerprint recognition for using camera. They affects fingerprint recognition, so this research is performed to measure and analyze these problems.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.8
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• pp.749-753
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• 2007

In this paper, a generalized version of the Motion Silhouette Image(MSI) called the Generic Motion Silhouette Image (GMSI) is proposed for gait recognition. The GMSI is a gray-level image and involves the spatiotemporal information of individual motion. The GMSI not only generalizes the MSI but also reflects a flexible feature of a gait sequence. Along with the GMSI, we use the Principal Component Analysis(PCA) to reduce the dimensionality of the GMSI and the Nearest Neighbor(NN) for classification. We apply the proposed feature to NLPR database and compare it with the conventional MSI. Experimental results show the effectiveness of the GMSI.

• Smart Media Journal
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• v.6 no.2
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• pp.15-25
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• 2017

The security and privacy issues derived from unsecurely storing biometrics templates in biometric authentication/ recognition systems have opened a new research area about how to secure the stored biometric templates. Biometrics-based key generation is the newest approach that provides not only a mechanism to protect stored biometric templates in authentication/ recognition systems, but also a method to integrate biometric systems with cryptosystems. Therefore, this approach has attracted much attention from researchers worldwide. A review of current research state to summarize the achievements and remaining works is necessary for further works. In this study, we first outlined the requirements and the primary challenges when implementing these systems. We then summarize the proposed techniques and achievements in representative studies on biometrics-based key generation. From that, we give a discussion about the accomplishments and remaining works with the corresponding challenges in order to provide a direction for further researches in this area.

• Journal of Information Processing Systems
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• v.16 no.1
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• pp.6-29
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• 2020

Biometrics identification using multiple modalities has attracted the attention of many researchers as it produces more robust and trustworthy results than single modality biometrics. In this paper, we present a novel multimodal recognition system that trains a deep learning network to automatically learn features after extracting multiple biometric modalities from a single data source, i.e., facial video clips. Utilizing different modalities, i.e., left ear, left profile face, frontal face, right profile face, and right ear, present in the facial video clips, we train supervised denoising auto-encoders to automatically extract robust and non-redundant features. The automatically learned features are then used to train modality specific sparse classifiers to perform the multimodal recognition. Moreover, the proposed technique has proven robust when some of the above modalities were missing during the testing. The proposed system has three main components that are responsible for detection, which consists of modality specific detectors to automatically detect images of different modalities present in facial video clips; feature selection, which uses supervised denoising sparse auto-encoders network to capture discriminative representations that are robust to the illumination and pose variations; and classification, which consists of a set of modality specific sparse representation classifiers for unimodal recognition, followed by score level fusion of the recognition results of the available modalities. Experiments conducted on the constrained facial video dataset (WVU) and the unconstrained facial video dataset (HONDA/UCSD), resulted in a 99.17% and 97.14% Rank-1 recognition rates, respectively. The multimodal recognition accuracy demonstrates the superiority and robustness of the proposed approach irrespective of the illumination, non-planar movement, and pose variations present in the video clips even in the situation of missing modalities.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.45 no.5
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• pp.192-200
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• 2008

Multimodal biometrics is a method for personal authentication and verification using more than two types of biometrics data. RBF based score-level fusion uses pattern recognition algorithm for multimodal biometrics, seeking the optimal decision boundary to classify score feature vectors each of which consists of matching scores obtained from several unimodal biometrics system for each sample. In this case, all matching scores are assumed to have the same reliability. However, in recent research it is reported that the quality of input sample affects the result of biometrics. Currently the matching scores having low reliability caused by low quality of samples are not currently considered for pattern recognition modelling in multimodal biometrics. To solve this problem, in this paper, we proposed the RBF based score-level fusion approach which employs quality information of input biometrics data to adjust decision boundary. As a result the proposed method with Qualify information showed better recognition performance than both the unimodal biometrics and the usual RBF based score-level fusion without using quality information.

• 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 KIEE Conference
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• 2002.11c
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• pp.313-316
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• 2002

The need to protect a main information and control a personal access using Biometrics in the intelligent building system is extended with growing the information and communication technologies in recent years. The fingerprint recognition technology is one of the biometrics methods available that has been widely used in various applications. But the present architecture of fingerprint recognition system has many of the problems because of centralized control network architecture. But we can solve the problems with distributed control network architecture. In this paper, the fingerprint recognition system uses the merit of the LonWorks. it is able to easily modularize the system and make up for the weak points in the typical fingerprint recognition system.

• Proceedings of the Korea Institutes of Information Security and Cryptology Conference
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• 2006.06a
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• pp.241-245
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• 2006

Recent studies show that fingerprint recognition technology is confronted with spoofing of artificial fingers. In order to overcome this problem, the fingerprint recognition system needs to distinguish a fake finger from a live finger. This paper examines existing software-based approaches for fingerprint liveness detection through experiments. Implemented and tested in this paper are the approaches based on deformation, wavelet, and perspiration. These approaches will be analyzed and compared based on experimental results.