• The Korean Journal of Food And Nutrition
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• v.33 no.4
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• pp.372-380
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• 2020

The purpose of this study is to investigate the physicochemical properties of jelly made from fermented red ginseng concentrate (FRGC) that can be easily absorbed and digested for the health promotion of the elderly. The pH of the jellies tended to decrease with increasing concentration of FRGC. Soluble solid content has significantly higher value when added more than 2%, and the water content of the sample was significantly lower when the FRGC was added 4%. As the amount of FRGC was increased, the total color difference increased, and the hardness of samples decreased significantly. On the other hand, the total ginsenoside contents of the FRGC was 45.50 mg/g. As the concentration of FRGC increased, the content of polyphenol and flavonoids increased. The increasing pattern of polyphenols and flavonoids showed a similar trend. As the content of FRGC increased, ABTS free radical scavenging activity significantly increased (p<0.05), and in the control, the minimum value (62.6 AEAC) and the 4% sample were highest (116.2 AEAC). DPPH radical scavenging activity was like that of ABTS radical scavenging activity. However, there was no significant difference in DPPH radical scavenging activity of 3% and 4% red ginseng jelly.

• The Magazine of the IEIE
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• v.39 no.2
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• pp.36-44
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• 2012

본 논문에서는 최근 얼굴 인식 평가에 많이 사용된 FRGC Ver 2.0 DB와 그 프로토콜을 간략히 소개하고 이를 이용한 다양한 얼굴 인식 방법 및 그 개발 동향에 대해서 살펴보고자 한다. FRGC는 객관적인 2D/3D 얼굴 인식 알고리즘 성능 평가를 위해서 공개되었는데, 본 논문에서는 2D 정면 얼굴 인식에 대한 내용을 위주로 기술하고자 한다. FRGC의 2D 얼굴 인식 DB는 주로 조명의 Control 유무에 따른 성능 비교를 위한 평가 프로토콜을 제안하고 있다. 이에 2004년부터 최근까지 10개 이상의 알고리즘이 발표되었고, 본 논문에서는 중요한 11개의 알고리즘을 살펴보고자 한다. 또한 이들 알고리즘에서 핵심적으로 사용되는 특징 추출 알고리즘을 살펴보고 마지막으로 각 알고리즘의 FRGC DB에서의 성능을 비교 평가하고자 한다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.12
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• pp.6133-6151
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• 2017

The field of periocular biometrics has gained wide attention as an alternative or supplemental means to conventional biometric traits such as the iris or the face. Periocular biometrics provide intermediate resolution between the iris and the face, which enables it to support both. We have developed a periocular recognition system by using uniform Multiscale Local Binary Pattern (uMLBP) and attribute features. The proposed system has been evaluated in terms of major factors that need to be considered on a mobile platform (e.g., distance and facial pose) to assess the feasibility of the use of periocular biometrics on mobile devices. Experimental results showed 98.7% of rank-1 identification accuracy on a subset of the Face Recognition Grand Challenge (FRGC) database, which is the best performance among similar studies.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.47 no.1
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• pp.44-52
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• 2010

In this paper, we propose a novel method for face recognition which uses polar coordinate instead of conventional cartesian coordinate. Among the central area of a face, we select a point as a pole and make a polar image of a face by evenly sampling pixels in each direction of 360 degrees around the pole. By applying conventional feature extraction methods to the polar image, the recognition rates are improved. The polar coordinate delineates near-pole area more vividly than the area far from the pole. In a face, important regions such as eyes, nose and mouth are concentrated on the central part of a face. Therefore, the polar coordinate of a face image can achieve more vivid representation of important facial regions compared to the conventional cartesian coordinate. The proposed polar coordinate transform was applied to Yale and FRGC databases and LDA and NLDA were used to extract features afterwards. The experimental results show that the proposed method performs better than the conventional cartesian images.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.49 no.4
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• pp.42-53
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• 2012

In pattern recognition and machine learning society, classification has been a classical problem and the most widely researched area. Adaptive boosting also known as Adaboost has been successfully applied to binary classification problems. It is a kind of boosting algorithm capable of constructing a strong classifier through a weighted combination of weak classifiers. On the other hand, the PCA and LDA algorithms are the most popular linear feature extraction methods used mainly for dimensionality reduction. In this paper, the combination of Adaboost and feature extraction methods is proposed for efficient classification of two class data. Conventionally, in classification problems, the roles of feature extraction and classification have been distinct, i.e., a feature extraction method and a classifier are applied sequentially to classify input variable into several categories. In this paper, these two steps are combined into one resulting in a good classification performance. More specifically, each projection vector is treated as a weak classifier in Adaboost algorithm to constitute a strong classifier for binary classification problems. The proposed algorithm is applied to UCI dataset and FRGC dataset and showed better recognition rates than sequential application of feature extraction and classification methods.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.10
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• pp.4903-4929
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• 2018

In this study, a fully automatic pose and expression invariant 3D face alignment algorithm is proposed to handle frontal and profile face images which is based on a two pass course to fine alignment strategy. The first pass of the algorithm coarsely aligns the face images to an intrinsic coordinate system (ICS) through a single 3D rotation and the second pass aligns them at fine level using a minimum nose tip-scanner distance (MNSD) approach. For facial recognition, multi-view faces are synthesized to exploit real 3D information and test the efficacy of the proposed system. Due to optimal separating hyper plane (OSH), Support Vector Machine (SVM) is employed in multi-view face verification (FV) task. In addition, a multi stage unified classifier based face identification (FI) algorithm is employed which combines results from seven base classifiers, two parallel face recognition algorithms and an exponential rank combiner, all in a hierarchical manner. The performance figures of the proposed methodology are corroborated by extensive experiments performed on four benchmark datasets: GavabDB, Bosphorus, UMB-DB and FRGC v2.0. Results show mark improvement in alignment accuracy and recognition rates. Moreover, a computational complexity analysis has been carried out for the proposed algorithm which reveals its superiority in terms of computational efficiency as well.