• Journal of the Institute of Electronics Engineers of Korea CI
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• v.48 no.3
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• pp.46-53
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• 2011

This paper proposes a SURF (Speeded Up Robust Features) based face recognition method which is one of typical interest point extraction algorithms. In general, SURF based object recognition is performed in interest point extraction and matching. In this paper, although, proposed method is employed not only in interest point extraction and matching, but also in face image rotation and interest point verification. image rotation is performed to increase the number of interest points and interest point verification is performed to find interest points which were matched correctly. Although proposed SURF based face recognition method requires more computation time than PCA based one, it shows better recognition rate than PCA algorithm. Through this experimental result, I confirmed that interest point extraction algorithm also can be adopted in face recognition.

• 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.

• Journal of KIISE:Software and Applications
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• v.31 no.6
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• pp.808-816
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• 2004

This paper proposes the method of implementation of practical online face verification system based on multiple feature combination and a similarity space. The main issue in face verification is to deal with the variability in appearance. It seems difficult to solve this issue by using a single feature. Therefore, combination of mutually complementary features is necessary to cope with various changes in appearance. From this point of view, we describe the feature extraction approaches based on multiple principal component analysis and edge distribution. These features are projected on a new intra-person/extra-person similarity space that consists of several simple similarity measures, and are finally evaluated by a support vector machine. From the experiments on a realistic and large database, an equal error rate of 0.029 is achieved, which is a sufficiently practical level for many real- world applications.

• Korean Journal of Cognitive Science
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• v.11 no.2
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• pp.79-87
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• 2000

In this paper we present a face recognition and face detection algorithm in MPEG compressed video. The proposed method consists three stage of processing steps. The first step is to produce a spatially reduced DC image form MPEG compressed video for processing. And the second step is face detection on reduced DC image. Finally, the last step is face recognition on partially extracted compressed frames which contain the detected faces. The spatially reduced DC image is produced from two dimensional inverse DCT of the DC coefficient and the first two AC coefficients. The face detection is performed on DC image and face recognition is performed on one extracted frame per GOP by using the K-L transform. In order to evaluate the proposed method, we carried out experiments on video database. The experiment results show the proposed method is very efficient and helpful for target tasks.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.12
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• pp.2547-2554
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• 2009

In this paper, we proposed a face detection algorithm and a hardware implementation method for ROI(Region Of Interest) of AF(Auto Focusing). We used face features in skin regions of YCbCr color space for face detection. The face features are the number of skin pixels in face regions, edge pixels in eye regions, and shadow pixels in lip regions. The each feature was statistically selected by 2,000 sample pictures of face. The proposed algorithm detects two faces that are closer center of the image for considering the effectiveness of hardware resource. The detected faces are displayed by rectangle for ROI of AF, and the rectangles are represented by positions in the image about starting point and ending point of the rectangles. The proposed face detection method was verified by using FPGA boards and mobile phone camera sensor.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.7
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• pp.896-902
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• 2021

Rotated face detection is required in many applications but still remains as a challenging task, due to the large variations of face appearances. In this paper, a polar coordinate transform that is not affected by rotation is proposed. In addition, a method for effectively detecting rotated faces using the transformed image has been proposed. The proposed polar coordinate transform maintains spatial information between facial components such as eyes, mouth, etc., since the positions of facial components are always maintained regardless of rotation angle, thereby eliminating rotation effects. Polar coordinate transformed images are trained using AdaBoost, which is used for frontal face detection, and rotated faces are detected. We validate the detected faces using LBP that trained the non-face images. Experiments on 3600 face images obtained by rotating images in the BioID database show a rotating face detection rate of 96.17%. Furthermore, we accurately detected rotated faces in images with a background containing multiple rotated faces.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09b
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• pp.162-165
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• 2003

본 논문에서는 웨이블렛 계수와 Hidden Markov Model(HMM)이용한 얼굴인식 알고리즘을 제안한다. 입력 영상은 이산웨이블렛을 기반으로 한 다해상도 분석기법을 사용하여 데이터 수를 압축한 후, 각각의 해상도에서 얻어진 웨이블렛 계수를 특징벡터로 사용하여 HMM의 모델을 생성한다. 인식단계 에서는 웨이블렛 변환에 의해 생성된 개별대역의 인식값을 더하여 상호 보완함으로써 인식률을 높일 수 있었다. 제안된 알고리즘의 타당성을 검증하기 위하여 기본적 알고리즘인 벡터 양자화(VQ) 기법을 적용한 경우와 기존 얼굴인식에 제안된 DCT-HMM을 이용한 기법과의 인식률 비교를 한 결과, 제안된 방법이 우수한 성능을 보임을 알 수 있었다.

• Korean Journal of Cognitive Science
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• v.3 no.1
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• pp.79-111
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• 1991

This paper suggests a system which analyzes facial images through hypotheses generation and verification.A model knowledge on faical structures is to be represented in a hieratchical frame system and the process of hypothesis generation and verification is to be embodied through linking upper lower-level frames.This paper especially addresses the problem of how to selec an approproate knowledge at each stage of hypotheses generation and verification.

• Journal of Korea Multimedia Society
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• v.4 no.5
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• pp.396-405
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• 2001

Usually it is difficult to extract facial regions in a complex image by using only a predetermined skin color. Expecially, it is more difficult to separate them from background regions that contains the skin color. This paper proposes a face detection method by using gradual range expansion of an initial skin color. By analyzing the skin color distribution several images that are collected in the Web, the range of dense distribution is selected as the range of the initial skin color. In each expanding step, expanded regions in the image are tested whether they can be actual facial regions by using the information of the shape of general face and the location of face organs. The shape of general face is modeled as an ellipse and the aspect ratio of its bounding box is used to define the shape constraint for faces. Only the eyes and lips are used as the face organs, which can be easily detected by extracting horizontal edges in the expanded regions. through several experiments, it is confirmed that the proposed method can detect exactly not only faces having partly distorted regions by highlight but also faces neighboring similar color regions.

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.5
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• pp.437-443
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• 2015

This paper presents face recognition based on the fusion of visible image and thermal infrared (IR) texture estimated from the face image in the visible spectrum. The proposed face recognition scheme uses a multi- layer neural network to estimate thermal texture from visible imagery. In the training process, a set of visible and thermal IR image pairs are used to determine the parameters of the neural network to learn a complex mapping from a visible image to its thermal texture in the low-dimensional feature space. The trained neural network estimates the principal components of the thermal texture corresponding to the input visible image. Extensive experiments on face recognition were performed using two popular face recognition algorithms, Eigenfaces and Fisherfaces for NIST/Equinox database for benchmarking. The fusion of visible image and thermal IR texture demonstrated improved face recognition accuracies over conventional face recognition in terms of receiver operating characteristics (ROC) as well as first matching performances.