• Journal of the Institute of Electronics Engineers of Korea SP
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• v.49 no.1
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• pp.81-87
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• 2012

The current defacing method for keeping an anonymity of brain images damages the integrity of a precise brain analysis due to over removal, although it maintains the patients' privacy. A novel method has been developed to create an anonymous face model while keeping the voxel values of an image exactly the same as that of the original one. The method contains two steps: construction of a mockup brain template from ten normalized brain images and a substitution of the mockup brain to the brain image. A level set segmentation algorithm is applied to segment a scalp-skull apart from the whole brain volume. The segmented mockup brain is coregistered and normalized to the subject brain image to create an anonymous face model. The validity of this modification is tested through comparing the intensity of voxels inside a brain area from the mockup brain with the original brain image. The result shows that the intensity of voxels inside from the mockup brain is same as ones from an original brain image, while its anonymity is guaranteed.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.24 no.8B
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• pp.1549-1557
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• 1999

In this paper, we propose a face recognition algorithm based on the differential image method-DCT This algorithm uses neural networks which is flexible for noise. Using the same condition (same luminous intensity and same distance from the fixed CCD camera to human face), we have captured two images. One doesn't contain human face. The other contains human face. Differential image method is used to separate the second image into face region and background region. After that, we have extracted square area from the face region, which is based on the edge distribution. This square region is used as the characteristics region of human face. It contains the eye bows, the eyes, the nose, and the mouth. After executing DCT for this square region, we have extracted the feature vectors. The feature vectors were normalized and used as the input vectors of the neural network. Simulation results show 100% recognition rate when face images were learned and 92.25% recognition rate when face images weren't learned for 30 persons.

• Journal of KIISE:Software and Applications
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• v.31 no.5
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• pp.605-616
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• 2004

A new practical implementation of a facial verification system using cumulative histogram of surface curvatures for the local and contour line areas is proposed, in this paper. The approach works by finding the nose tip that has a protrusion shape on the face. In feature recognition of 3D face images, one has to take into consideration the orientated frontal posture to normalize after extracting face area from the original image. The feature vectors are extracted by using the cumulative histogram which is calculated from the curvature of surface for the contour line areas: 20, 30 and 40, and nose, mouth and eyes regions, which has depth and surface characteristic information. The L1 measure for comparing two feature vectors were used, because it was simple and robust. In the experimental results, the maximum curvature achieved recognition rate of 96% among the proposed methods.

• Journal of Korea Multimedia Society
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• v.8 no.1
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• pp.34-45
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• 2005

In this paper, a novel implementation of a person verification system based on depth-weighted Hausdorff distance (DWHD) using the surface curvature of the face is proposed. The definition of Hausdorff distance is a measure of the correspondence of two point sets. The approach works by finding the nose tip that has a protrusion shape on the face. In feature recognition of 3D face image, one has to take into consideration the orientated frontal posture to normalize after extracting face area from original image. The binary images are extracted by using the threshold values for the curvature value of surface for the person which has differential depth and surface characteristic information. The proposed DWHD measure for comparing two pixel sets were used, because it is simple and robust. In the experimental results, the minimum curvature which has low pixel distribution achieves recognition rate of 98% among the proposed methods.

• Journal of the Korea Society of Computer and Information
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• v.15 no.11
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• pp.67-73
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• 2010

In this paper, a new face detection method that is more accurate than the conventional methods is proposed. This method utilizes a hybrid of Hausdorff distance based on the geometric similarity between the two sets of points and the LBP distance based on the distribution of local micro texture of an image. The parameters for normalization and the optimal blending factor of the two different metrics were calculated from training sample images. Popularly used face database was used to show that the proposed method is more effective and robust to the variation of the pose, illumination, and back ground than the methods based on the Hausdorff distance or LBP distance. In the particular case, the average error distance between the detected and the true face location was reduced to 47.9% of the result of LBP method, and 22.8% of the result of Hausdorff method.

• The Journal of the Korea institute of electronic communication sciences
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• v.10 no.11
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• pp.1307-1314
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• 2015

This study proposed 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. The algorithm proposed detects the face area alone after normalization through pre-processing and obtains a feature vector using (PCA). Also, by applying the feature vector obtained for SVM, face areas can be tested. After the testing, the feature vector is applied to LDA and using Euclidean distance in the 2nd dimension, the final analysis and matching is performed. The algorithm proposed in this study could increase the stability and accuracy of recognition rates and as a large amount of calculation was not necessary due to the use of two dimensions, real-time recognition was possible.

• The KIPS Transactions:PartA
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• v.8A no.1
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• pp.9-15
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• 2001

영상에 관련된 다양한 응용 시스템들을 구현하는 많은 연구들이 진행되어 왔지만, 그러한 영상 관련 응용 시스템을 구현함에 있어서 처리속도의 저하로 인하여 많은 어려움을 겪고 있다. 이를 해결하기 위해 대두된 여러 방법들 중에서 최근 하드웨어 접근 방법에 고려한 많은 관심과 연구가 진행되고 있다. 본 논문은 영상을 실시간으로 처리하기 위하여 하드웨어 구조를 갖는 병렬처리시스템을 기술하며, 또한 병렬처리시스템을 얼굴 검색 시스템에 적용한 후 처리속도 및 실험 결과를 기술한다. 병렬처리시스템은 SIMD와 MIMD가 결합된 구조를 갖고 있기 때문에 다양한 영상 응용시스템에 대해서 융통성과 효율성을 제공하며, 144개의 처리기와 12개의 다중접근기억장치, 외부 메모리 모듈을 위한 인터페이스와 외부 프로세서 장치(i960Kx)와의 통신을 위한 인터페이스로 구성되어있다. 다중접근기억장치는 메모리 모듈선택회로, 데이터 라이팅회로, 그리고, 주소계산 및 라우팅회로로 구성되어 있다. 또한 얼굴 검색 시스템을 병렬처리 시스템에 적합한 병렬화를 제공하기 위해 메쉬방법을 이용하여 전처리, 정규화, 4개 특징값 추출, 그리고 분류화로 구성하였다. 병렬처리시스템은 하드웨어 모의실험 패키지인 CADENCE사의 Verilog-XL로 모의실험을 수행하여 기능과 성능을 검증하였다.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2007.04a
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• pp.153-156
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• 2007

인간의 시각 시스템은 선택적 주의 집중에 의해 시각 수용체로 도달되는 많은 물체들 중에서 필요한 정보만을 추출하여 원하는 작업을 수행한다. Itti와 Koch는 시각적 주의를 제어할 수 있는, 신경계를 모방한 계산적 모델을 제안하였으나 조명환경에 고정적인 saliency map을 구성하였다. 따라서, 본 논문에서는 영상에서 ROI(region of interest)을 탐지하기 위한 조명환경에 적응적인 saliency map 모델을 구성하는 기법을 제시한다. 변화하는 환경에서 원하는 특징을 부각시키기 위하여 상황에 적응적인 동적 가중치를 부여한다. 동적 가중치는 conspicuity map에 S.K. Chang이 제안한 PIM(Picture Information Measure)을 적용시켜 정보량을 측정한 후, 이에 따라 정규화된 값을 부여함으로써 구현한다. 제안하는 조명환경에 강인한 적응적인 saliency map 모델 구현의 성능을 얼굴검출 실험을 통하여 검증하였다.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.42 no.1
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• pp.17-23
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• 2005

In this paper, we propose face recognition method using embedded data in super states segmentalized that is specification region exist to face region, hair, forehead, eyes, ears, nose, mouth, and chin. Proposed method defines super states that is specification area in normalized size (92×112), and embedded data that is extract internal factor in super states segmentalized achieve face recognition by PCA algorithm. Proposed method can receive specification data that is less in proposed image's size (92×112) because do orignal image to learn embedded data not to do all loaming. And Showed face recognition rate in image of 92×112 size averagely 99.05%, step 1 99.05%, step 2 98.93%, step 3 98.54%, step 4 97.85%. Therefore, method that is proposed through an experiment showed that the processing speed improves as well as reduce existing face image's information.

• Journal of the Institute of Electronics Engineers of Korea TE
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• v.37 no.3
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• pp.104-113
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• 2000

In this study, we propose facial image recognition based on wavelet transform and neural network. This algorithm is proposed by following processes. First, two gray level images is captured in constant illumination and, after removing input image noise using a gaussian filter, differential image is obtained between background and face input image, and this image has a process of erosion and dilation. Second, a mask is made from dilation image and background and facial image is divided by projecting the mask into face input image Then, characteristic area of square shape that consists of eyes, a nose, a mouth, eyebrows and cheeks is detected by searching the edge of divided face image. Finally, after characteristic vectors are extracted from performing discrete wavelet transform(DWT) of this characteristic area and is normalized, normalized vectors become neural network input vectors. And recognition processing is performed based on neural network learning. Simulation results show recognition rate of 100 % about learned image and 92% about unlearned image.