• 한국컴퓨터정보학회논문지
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• 제10권5호
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• pp.353-362
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• 2005

최근 들어 바이모달 인식에 관한 연구가 활발히 진행되고 있다. 본 논문에서는 음성 정보와 얼굴정보를 이용하여 바이모달 시스템을 구현하였다. 얼굴인식은 얼굴 검출과 얼굴 인식 두 부분으로 나누어서 실험을 하였다. 얼굴 검출 단계에서는 AdaBoost를 이용하여 얼굴 후보 영역을 검출 한 뒤 PCA를 통해 특징 벡터 계수를 줄였다. PCA를 통해 추출된 특징 벡터를 객체 분류 기법인 SVM을 이용하여 얼굴을 검출 및 인식하였다. 음성인식은 MFCC를 이용하여 음성 특징 추출을 하였으며 HMM을 이용하여 음성인식을 하였다. 인식결과, 단일 인식을 사용하는 것보다 얼굴과 음성을 같이 사용하였을 때 인식률의 향상을 가져왔고, 잡음 환경에서는 더욱 높은 성능을 나타냈었다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권2호
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• pp.810-831
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• 2019

Recently, continuous dimensional emotion recognition from audiovisual clues has attracted increasing attention in both theory and in practice. The large amount of data involved in the recognition processing decreases the efficiency of most bimodal information fusion algorithms. A novel algorithm, namely the incomplete Cholesky decomposition based kernel cross factor analysis (ICDKCFA), is presented and employed for continuous dimensional audiovisual emotion recognition, in this paper. After the ICDKCFA feature transformation, two basic fusion strategies, namely feature-level fusion and decision-level fusion, are explored to combine the transformed visual and audio features for emotion recognition. Finally, extensive experiments are conducted to evaluate the ICDKCFA approach on the AVEC 2016 Multimodal Affect Recognition Sub-Challenge dataset. The experimental results show that the ICDKCFA method has a higher speed than the original kernel cross factor analysis with the comparable performance. Moreover, the ICDKCFA method achieves a better performance than other common information fusion methods, such as the Canonical correlation analysis, kernel canonical correlation analysis and cross-modal factor analysis based fusion methods.

• ETRI Journal
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• 제29권3호
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• pp.399-401
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• 2007

With a growing emphasis on human identification, iris recognition has recently received increasing attention. Iris recognition includes eye imaging, iris segmentation, verification, and so on. In this letter, we propose a novel and efficient iris recognition method which employs a cumulative-sum-based grey change analysis. Experimental results demonstrate that the proposed method can be used for human identification in efficient manner.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2003년도 하계종합학술대회 논문집 Ⅳ
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• pp.2263-2266
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• 2003

Three-dimensional face recognition algorithm using curvature information representing characteristics of surface form is suggested. The experiment showed more than 90 percent of recognition for the noses which had definite change value of data, and contained much information about surface curvature. Recognition ratio using a contour taken from the remaining part other than the eyes, noses, mouths which are the main components of faces showed the important role, which could be used as the important index information in the three-dimensional face recognition.

• 한국통신학회논문지
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• 제35권12C호
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• pp.984-994
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• 2010

Pose variation is a critical problem in face recognition. Three-dimensional(3D) face recognition techniques have been proposed, as 3D data contains depth information that may allow problems of pose variation to be handled more effectively than with 2D face recognition methods. This paper proposes a pose-normalized 3D face modeling method that translates and rotates any pose angle to a frontal pose using a plane fitting method by Singular Value Decomposition(SVD). First, we reconstruct 3D face data with stereo vision method. Second, nose peak point is estimated by depth information and then the angle of pose is estimated by a facial plane fitting algorithm using four facial features. Next, using the estimated pose angle, the 3D face is translated and rotated to a frontal pose. To demonstrate the effectiveness of the proposed method, we designed 2D and 3D face recognition experiments. The experimental results show that the performance of the normalized 3D face recognition method is superior to that of an un-normalized 3D face recognition method for overcoming the problems of pose variation.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.134-138
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• 2005

There are several application have been developed on internet-based speech recognition. Internet-based speech recognition is a distributed application and there were various techniques and methods have been using for that purposed. Currently, client-server paradigm was one of the popular technique that been using for client-server communication in web application. However, there is a new paradigm with the same purpose: mobile agent technology. Mobile agent technology has several advantages working on distributed internet-based system. This paper presents, applying mobile agent technology in internet-based speech recognition which based on client-server processing architecture.

• Journal of Information Processing Systems
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• 제8권3호
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• pp.483-494
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• 2012

Many recent events, such as terrorist attacks, exposed defects in most sophisticated security systems. Therefore, it is necessary to improve security data systems based on the body or behavioral characteristics, often called biometrics. Together with the growing interest in the development of human and computer interface and biometric identification, human face recognition has become an active research area. Face recognition appears to offer several advantages over other biometric methods. Nowadays, Principal Component Analysis (PCA) has been widely adopted for the face recognition algorithm. Yet still, PCA has limitations such as poor discriminatory power and large computational load. This paper proposes a novel algorithm for face recognition using a mid band frequency component of partial information which is used for PCA representation. Because the human face has even symmetry, half of a face is sufficient for face recognition. This partial information saves storage and computation time. In comparison with the traditional use of PCA, the proposed method gives better recognition accuracy and discriminatory power. Furthermore, the proposed method reduces the computational load and storage significantly.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권12호
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• pp.6063-6079
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• 2019

When the length of sampling data sequence is too large, the method of gesture recognition based on traditional Dynamic Time Warping (DTW) algorithm will lead to too long calculation time, and the accuracy of recognition result is not high.Support vector machine (SVM) has some shortcomings in precision, Edit Distance on Real Sequences(EDR) algorithm does not guarantee that noise suppression will not suppress effective data.A new method based on Improved Interpolation Dynamic Time Warping (IIDTW)algorithm is proposed to improve the efficiency of gesture recognition and the accuracy of gesture recognition. The results show that the computational efficiency of IIDTW algorithm is more than twice that of SVM-DTW algorithm, the error acceptance rate is FAR reduced by 0.01%, and the error rejection rate FRR is reduced by 0.5%.Gesture recognition based on IIDTW algorithm can achieve better recognition status. If it is applied to unlock mobile phone, it is expected to become a new generation of unlock mode.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제5권1호
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• pp.1-6
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• 2005

In this paper, we propose an emotion recognition method using the facial images and speech signals. Six basic emotions including happiness, sadness, anger, surprise, fear and dislike are investigated. Facia] expression recognition is performed by using the multi-resolution analysis based on the discrete wavelet. Here, we obtain the feature vectors through the ICA(Independent Component Analysis). On the other hand, the emotion recognition from the speech signal method has a structure of performing the recognition algorithm independently for each wavelet subband and the final recognition is obtained from the multi-decision making scheme. After merging the facial and speech emotion recognition results, we obtained better performance than previous ones.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제9권2호
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• pp.763-774
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• 2015

Hand posture recognition has been a wide region of applications in Human Computer Interaction and Computer Vision for many years. The problem arises mainly due to the high dexterity of hand and self-occlusions created in the limited view of the camera or illumination variations. To remedy these problems, a hand posture recognition method using 3-D point cloud is proposed to explicitly utilize 3-D information from depth maps in this paper. Firstly, hand region is segmented by a set of depth threshold. Next, hand image normalization will be performed to ensure that the extracted feature descriptors are scale and rotation invariant. By robustly coding and pooling 3-D facets, the proposed descriptor can effectively represent the various hand postures. After that, SVM with Gaussian kernel function is used to address the issue of posture recognition. Experimental results based on posture dataset captured by Kinect sensor (from 1 to 10) demonstrate the effectiveness of the proposed approach and the average recognition rate of our method is over 96%.