• 음성과학
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• 제13권4호
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• pp.177-186
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• 2006

In an effort to enhance the quality of feature vector classification and thereby reduce the recognition error rate of the speaker-independent speech recognition, we employ the Mahalanobis distance in the calculation of the similarity measure between feature vectors. It is assumed that the metric matrix of the Mahalanobis distance be diagonal for the sake of cost reduction in memory and time of calculation. We propose that the diagonal elements be given in terms of the variations of the feature vector components. Geometrically, this prescription tends to redistribute the set of data in the shape of a hypersphere in the feature vector space. The idea is applied to the speech recognition by hidden Markov model with fuzzy vector quantization. The result shows that the recognition is improved by an appropriate choice of the relevant adjustable parameter. The Viterbi score difference of the two winners in the recognition test shows that the general behavior is in accord with that of the recognition error rate.

• 한국컴퓨터정보학회:학술대회논문집
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• 한국컴퓨터정보학회 2008년도 제39차 동계학술발표논문집 16권2호
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• pp.49-54
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• 2009

A fuzzy weighted mean method on a 2D shape recognition system is introduced in this paper. The bispectrum based on third order cumulant is applied to the contour sequence of each image for the extraction of a feature vector. This bispectral feature vector, which is invariant to shape translation, rotation and scale, represents a 2D planar image. However, to obtain the best performance, it should be considered certain criterion on the calculation of weights for the fuzzy weighted mean method. Therefore, a new method to calculate weights using means by differences of feature values and their variances with the maximum distance from differences of feature values. is developed. In the experiments, the recognition results with fifteen dimensional bispectral feature vectors, which are extracted from 11.808 aircraft images based on eight different styles of reference images, are compared and analyzed.

• 한국인터넷방송통신학회논문지
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• 제16권6호
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• pp.183-190
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• 2016

논문에서는 얼굴 인식을 위한 보다 안정적이며 조명 변화와 회전에 강인하게 얼굴 영역을 검출하며, 계산의 효율성과 검출 성능을 동시에 만족시키는 강인한 인식 알고리즘에 대해 제안한다. 제안하는 알고리즘은 전처리 과정을 거쳐 정규화한 후 얼굴 영역만을 분할 검출한 후 주성분분석(PCA)을 이용하여 특징벡터를 구한다. 또한 구해진 특징벡터를 SVM에 적용하여 최적의 이진분류를 진행함으로써 얼굴 영역에 대한 검증을 수행한다. 검증 후 특징벡터를 이용하여 최종 얼굴을 인식하게 된다. 본 논문에서 제안하는 방법은 인식률의 안전성과 정확성을 향상시킬 수 있었으며, 차원 축소로 인해 많은 계산 량이 요구되지 않기 때문에 실시간 인식도 가능하다.

• International journal of advanced smart convergence
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• 제7권4호
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• pp.40-49
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• 2018

We propose a more stable robust recognition algorithm which detects faces reliably even in cases where there are changes in lighting and angle of view, as well it satisfies efficiency in calculation and detection performance. We propose detects the face area alone after normalization through pre-processing and obtains a feature vector using (PCA). The feature vector is applied to LDA and using Euclidean distance of intra-class variance and inter class variance in the 2nd dimension, the final analysis and matching is performed. Experimental results show that the proposed method has a wider distribution when the input image is rotated $45^{\circ}$ left / right. We can improve the recognition rate by applying this feature value to a single algorithm and complex algorithm, and it is possible to recognize in real time because it does not require much calculation amount due to dimensional reduction.

• 음성과학
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• 제15권3호
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• pp.7-15
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• 2008

This paper proposes the method to extract new feature vectors using the difference between the cepstrum for static characteristics and delta cepstrum for dynamic characteristics in speaker recognition (SR). The difference vector (DV) which it proposes from this paper is containing the static and the dynamic characteristics simultaneously at the intermediate characteristic vector which uses the deference between the static and the dynamic characteristics and as the characteristic vector which is new there is a possibility of doing. Compared to the conventional method, the proposed method can achieve new feature vector without increasing of new parameter, but only need the calculation process for the difference between the cepstrum and delta cepstrum. Experimental results show that the proposed method has a good performance more than 2.03%, on average, compared with conventional method in speaker identification (SI).

• 한국정보통신학회:학술대회논문집
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• 한국해양정보통신학회 2001년도 추계종합학술대회
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• pp.579-582
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• 2001

벡터변형은 벡터 양자화(VQ)와 부호화를 통합한 새로운 방법이다. 최근까지 부호화에 적용된 코드북 생성은 LBG 알고리즘이었으나 신경회로망을 기반으로 한 자기생성 특성맵(SOFM: Self Organizing Feature Map)의 장점을 이용하면 시스템의 성능을 개선할 수 있다는 점에 착안하였다. 본 논문에서는 SOFM 알고리즘을 적용한 VTC(Vector Transformation coding)코드북 생성과 LBG 알고리즘의 부호화률에 대한 결과를 비교하여 분석하였다. 벡터 양자화의 문제점은 계산의 복잡성과 코드북 생성에 있으므로 본 연구에서는 이 문제의 해결을 위해 신경망 접근법을 제안한다.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 1999년도 추계종합학술대회 논문집
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• pp.581-584
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• 1999

In order to retrieve the rotated image within database by the content based image retrieval system, the algorithms with rotation robustness is usually applied in the procedure of the feature extraction. In that case, it requires much calculation time for feature extraction and much indexed data for feature indexing. Thus. in this paper. we propose the rotation robust algorithm using the block variance of the projected vector. The algorithm does not require additional calculation for feature extraction and is executed within query time by comparing the extracted data. Proposed method can be processed through database including various size of images with shape information and executed with fast response time in implementation.

• Journal of Multimedia Information System
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• 제6권1호
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• pp.15-22
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• 2019

There are many ways to perform face recognition. The first step of face recognition is the face detection step. If the face is not found in the first step, the face recognition fails. Face detection research has many difficulties because it can be varied according to face size change, left and right rotation and up and down rotation, side face and front face, facial expression, and light condition. In this study, facial features are extracted and the extracted features are geometrically reconstructed in order to improve face recognition rate in extracted face region. Also, it is aimed to adjust face angle using reconstructed facial feature vector, and to improve recognition rate for each face angle. In the recognition attempt using the result after the geometric reconstruction, both the up and down and the left and right facial angles have improved recognition performance.

• 한국전자통신학회논문지
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• 제10권11호
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• pp.1307-1314
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• 2015

본 논문에서는 얼굴 인식을 위한 보다 안정적이며 조명 변화와 회전에 강인하게 얼굴 영역을 검출하며, 계산의 효율성과 검출 성능을 동시에 만족시키는 강인한 인식 알고리즘에 대해 제안한다. 제안하는 알고리즘은 전처리 과정을 거쳐 정규화한 후 얼굴 영역만을 분할 검출한 후 주성분분석(PCA)을 이용하여 특징벡터를 구한다. 또한 구해진 특징벡터를 SVM에 적용하여 최적의 이진분류를 진행함으로써 얼굴 영역에 대한 검증을 수행한다. 검증 후 특징벡터를 다시 LDA에 적용하여 2차원 공간상에서 유클리디안 거리 이용하여 최종 얼굴을 인식하게 된다. 본 논문에서 제안하는 방법으로 인식률의 안전성과 정확성을 향상시킬 수 있었으며, 차원 축소로 인해 많은 계산 량이 요구되지 않기 때문에 실시간 인식도 가능하다.

• 대한의용생체공학회:의공학회지
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• 제32권4호
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• pp.295-304
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• 2011

In this study, we evaluated the EEG based BCI algorithm using common spatial pattern to find realistic applicability using neurofeedback EEG based BCI algorithm - EEG mode, feature vector calculation, the number of selected channels, 3 types of classifier, window size is evaluated for 10 subjects. The experimental results have been evaluated depending on conditioned experiment whether neurofeedback is used or not In case of using neurofeedback, a few subjects presented exceptional but general tendency presented the performance improvement Through this study, we found a motivation of development for the specific classifier based BCI system and the assessment evaluation system. We proposed a need for an optimized algorithm applicable to the robust motor imagery evaluation system with more useful functionalities.