• The Journal of the Korea Contents Association
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• v.11 no.11
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• pp.10-18
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• 2011

Medical asset protection is important in each medical institution especially because of the law on private medical record protection and face recognition for this protection is one of the most interesting and challenging problems. In recognizing human faces, the distortion of face images can be caused by the change of pose, illumination, expressions and scale. It is difficult to recognize faces due to the locations of lights and the directions of lights. In order to overcome those problems, this paper presents an analysis of coefficients of Gabor wavelets, kernel decision, feature point, size of kernel, for face recognition in CCTV surveillance. The proposed method consists of analyses. The first analysis is to select of the kernel from images, the second is an coefficient analysis for kernel sizes and the last is the measure of changes in garbo kernel sizes according to the change of image sizes. Face recognitions are processed using the coefficients of experiment results and success rate is 97.3%. Ultimately, this paper suggests empirical application to verify the adequacy and the validity with the proposed method. Accordingly, the satisfaction and the quality of services will be improved in the face recognition area.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.42 no.2 s.302
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• pp.121-130
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• 2005

In this paper, we propose a feature extraction method for iris recognition using wavelet transforms. The wavelet transform is fast and has a good localization characteristic. In particular, the low frequency band can be used as an effective feature vector. In iris recognition, the noise caused by eyelid the eyebrow, glint, etc may be included in iris. The iris pattern is distorted by noises by itself, and a feature extraction algorithm based on filter such as Wavelets, Gabor transform spreads noises into whole iris region. Namely, such noises degrade the performance of iris recognition systems a major problem. This kind of noise has adverse effect on performance. In order to solve these problems, we propose to divide the iris image into a number of sub-region and apply the wavelet transform to each sub-region. Experimental results show that the performance of proposed method is comparable to existing methods using Gabor transform and region division noticeably improves recognition performance. However, it is noted that the processing time of the wavelet transform is much faster than that of the existing methods.

• The Journal of the Acoustical Society of Korea
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• v.21 no.4E
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• pp.156-163
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• 2002

We apply independent component analysis (ICA) for extracting an optimal basis to the problem of finding efficient features for representing speech signals of a given speaker The speech segments are assumed to be generated by a linear combination of the basis functions, thus the distribution of speech segments of a speaker is modeled by adapting the basis functions so that each source component is statistically independent. The learned basis functions are oriented and localized in both space and frequency, bearing a resemblance to Gabor wavelets. These features are speaker dependent characteristics and to assess their efficiency we performed speaker identification experiments and compared our results with the conventional Fourier-basis. Our results show that the proposed method is more efficient than the conventional Fourier-based features in that they can obtain a higher speaker identification rate.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2019.11a
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• pp.106-109
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• 2019

최근 Convolutional Neural Network (CNN)에 영상이 아닌 비학습적 알고리즘으로부터 도출된 특징맵을 입력함으로써 영상처리 성능 및 계산자원 효율성 향상을 이룬 보고가 늘어나고 있다. 본 논문에서는 이러한 점을 바탕으로 가보웨이블릿 특징맵을 입력으로 하는 CNN 기반 영상잡음제거기를 제안하고 그 성능 및 특징을 고찰하였다. 즉 기존의 CNN 에서는 일반적인 영상을 입력하는 반면에 본 논문에서는 영상으로부터 추출한 웨이블릿 계수들을 입력하였고, 이를 통하여 기존의 방법에 비하여 성능을 유지하면서 계산량을 줄일 수 있는 가능성을 확인하였다.

• The Journal of the Acoustical Society of Korea
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• v.21 no.4
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• pp.156-156
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• 2002

We apply independent component analysis (ICA) for extracting an optimal basis to the problem of finding efficient features for representing speech signals of a given speaker The speech segments are assumed to be generated by a linear combination of the basis functions, thus the distribution of speech segments of a speaker is modeled by adapting the basis functions so that each source component is statistically independent. The learned basis functions are oriented and localized in both space and frequency, bearing a resemblance to Gabor wavelets. These features are speaker dependent characteristics and to assess their efficiency we performed speaker identification experiments and compared our results with the conventional Fourier-basis. Our results show that the proposed method is more efficient than the conventional Fourier-based features in that they can obtain a higher speaker identification rate.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.3
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• pp.205-208
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• 2013

Generally, binary pattern transforms have been used in the field of the face recognition and facial expression, since they are robust to illumination. Thus, this paper proposes an illumination-robust face recognition system combining an MLDP, which improves the texture component of the LDP, and a 2D-PCA algorithm. Unlike that binary pattern transforms such as LBP and LDP were used to extract histogram features, the proposed method directly uses the MLDP image for feature extraction by 2D-PCA. The performance evaluation of proposed method was carried out using various algorithms such as PCA, 2D-PCA and Gabor wavelets-based LBP on Yale B and CMU-PIE databases which were constructed under varying lighting condition. From the experimental results, we confirmed that the proposed method showed the best recognition accuracy.