• 한국통신학회논문지
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• 제35권7B호
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• pp.1058-1065
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• 2010

최근 유비쿼터스 컴퓨팅과 유비쿼터스 네트워크를 활용하여 새로운 서비스들을 개발하려는 노력이 진행 중이며, 이에 관련된 기술의 중요성도 급증하고 있다. 특히 실내에서 사물의 정밀한 위치를 인식하는 기술은 유비쿼터스 서비스를 제공함에 있어 핵심 기술로 떠오르면서, 이에 대한 연구가 활발히 진행 중이다. 이러한 많은 관심에도 불구하고, 지금까지 실내 위치인식을 위한 시뮬레이션 툴은 그 중요성에 비해 연구가 미진한 실정이다. 본 논문에서는 고속의 근거리 무선 통신망을 제공할 수 있는 해결책으로 최근 각광을 받고 있는 UWB 무선 채널 상에서 정밀한 실내 위치인식을 위해 Ptolemy 툴을 이용한 범용적인 위치인식 시뮬레이터를 개발하고 다양한 위치인식 알고리즘들의 성능을 분석하고자 한다.

• 정보처리학회논문지B
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• 제12B권2호
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• pp.109-114
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• 2005

생체(biometric) 정보를 이용한 사용자 인중 기술에 대한 관심이 고조되고 있으며, 이 중에서 얼굴인식은 비접촉이라는 장점 때문에 최근 생체인식 분야에서 연구가 활발한 분야 중에 하나이다. 본 논문에서는 얼굴인식의 선행작업인 얼굴검출단계에서 효과적으로 눈위치를 추출하는 방법을 제안한다. 눈 위치 추출을 위하여 영상에 대한 이진화를 반복적 임계치 설정 방법을 통하여 수행하며, 눈의 특성 강화를 위한 가우시안 필터를 사용하여 눈의 위치를 추출하고, 상관관계를 이용하여 추출된 눈의 위치에 대한 검증 단계를 거친다. 논문에서 제안한 눈 위치 추출은 정확도뿐만 아니라, 온라인 시스템에 적용 가능하도록 고려하였고, 온라인 시스템에 적용한 결과 만족할 만한 성능을 보였다.

• 디지털콘텐츠학회 논문지
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• 제15권5호
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• pp.579-586
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• 2014

본 논문은 문맥-자유 문법 (context-free grammar)를 이용하여, 차선변경 상황에서의 운전자의 행동패턴 인식을 하는 방법을 제안하는 것을 목표로 한다. 문맥-자유-문법은 기존 패턴인식 방식과는 대조적으로 유한적 기호로는 쉽게 표현될 수 없는 특징들을 비교적 손쉽게 표현할 수 있다. 이 방식을 적용하여, 동시에 여러 특징을 각각 고려해야 하는 좌표기반 데이터 처리 대신 심볼 시퀀스 방식 (symbolic sequence)을 패턴화하기 위해 구문론적 방식을 적용한다. 이 방법은 운전자와 안전 운전 분야 연구자들에게 효율적이고 보다 직관적인 방법으로 보다 더 효과적인 수행에 도움이 된다. 본 연구의 향후과제로 보다 안정적인 인식률을 획득하기 위해 확률적 구문분석 방법을 적용할 계획이다.

• International Journal of Computer Science & Network Security
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• 제21권8호
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• pp.288-296
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• 2021

Automated face recognition in a runtime environment is gaining more and more important in the fields of surveillance and urban security. This is a difficult task keeping in mind the constantly volatile image landscape with varying features and attributes. For a system to be beneficial in industrial settings, it is pertinent that its efficiency isn't compromised when running on roads, intersections, and busy streets. However, recognition in such uncontrolled circumstances is a major problem in real-life applications. In this paper, the main problem of face recognition in which full face is not visible (Occlusion). This is a common occurrence as any person can change his features by wearing a scarf, sunglass or by merely growing a mustache or beard. Such types of discrepancies in facial appearance are frequently stumbled upon in an uncontrolled circumstance and possibly will be a reason to the security systems which are based upon face recognition. These types of variations are very common in a real-life environment. It has been analyzed that it has been studied less in literature but now researchers have a major focus on this type of variation. Existing state-of-the-art techniques suffer from several limitations. Most significant amongst them are low level of usability and poor response time in case of any calamity. In this paper, an improved face recognition system is developed to solve the problem of occlusion known as FRS-OCC. To build the FRS-OCC system, the color and texture features are used and then an incremental learning algorithm (Learn++) to select more informative features. Afterward, the trained stack-based autoencoder (SAE) deep learning algorithm is used to recognize a human face. Overall, the FRS-OCC system is used to introduce such algorithms which enhance the response time to guarantee a benchmark quality of service in any situation. To test and evaluate the performance of the proposed FRS-OCC system, the AR face dataset is utilized. On average, the FRS-OCC system is outperformed and achieved SE of 98.82%, SP of 98.49%, AC of 98.76% and AUC of 0.9995 compared to other state-of-the-art methods. The obtained results indicate that the FRS-OCC system can be used in any surveillance application.

• Journal of Information Processing Systems
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• 제16권1호
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• pp.6-29
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• 2020

Biometrics identification using multiple modalities has attracted the attention of many researchers as it produces more robust and trustworthy results than single modality biometrics. In this paper, we present a novel multimodal recognition system that trains a deep learning network to automatically learn features after extracting multiple biometric modalities from a single data source, i.e., facial video clips. Utilizing different modalities, i.e., left ear, left profile face, frontal face, right profile face, and right ear, present in the facial video clips, we train supervised denoising auto-encoders to automatically extract robust and non-redundant features. The automatically learned features are then used to train modality specific sparse classifiers to perform the multimodal recognition. Moreover, the proposed technique has proven robust when some of the above modalities were missing during the testing. The proposed system has three main components that are responsible for detection, which consists of modality specific detectors to automatically detect images of different modalities present in facial video clips; feature selection, which uses supervised denoising sparse auto-encoders network to capture discriminative representations that are robust to the illumination and pose variations; and classification, which consists of a set of modality specific sparse representation classifiers for unimodal recognition, followed by score level fusion of the recognition results of the available modalities. Experiments conducted on the constrained facial video dataset (WVU) and the unconstrained facial video dataset (HONDA/UCSD), resulted in a 99.17% and 97.14% Rank-1 recognition rates, respectively. The multimodal recognition accuracy demonstrates the superiority and robustness of the proposed approach irrespective of the illumination, non-planar movement, and pose variations present in the video clips even in the situation of missing modalities.

• Journal of Multimedia Information System
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• 제7권4호
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• pp.239-248
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• 2020

The performance of the facial recognition system is determined by many technical factors. Further, most of the technical factors have been realized or are still in continued research. The recognition rate has a great influence on performance not only by technical factors but also by other factors. However, researchers are trying to improve the recognition rate by focusing only on technical factors. The mechanism of recognizing is to compare a face image obtained by photography to an already stored face image and determine the score of the similarity. However, if the photographed image is damaged by external light, even a system with a good algorithm will fail to recognize it. Therefore, it is important to prevent the disturbance of light entering from the outside, so it should be blocked, but the camera will not work without light. Thus, it is proposed that a method to secure the external light but block the disturbance of light that affects photography. A method of blocking disturbance light is to use a polarization filter. There are three polarization methods: circular polarization, linear polarization, and elliptical polarization. In this paper, an experiment was performed to overcome disturbance of light using only a circularly polarized filter. In addition, a lighting system that reproduces disturbance light was provided for the experiment, and light of varying intensities and angles was installed to affect the face recognition camera. As a result of actual application, it was determined that a very improved recognition performance in various disturbance light environments.

• 한국시뮬레이션학회논문지
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• 제20권4호
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• pp.13-20
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• 2011

현재 일상생활에서 얼굴 인식은 신원확인, 보안 등의 목적으로 사용되고 있다. 얼굴인식의 과정은 첫 번째로 얼굴이미지의 특징을 추출해야 한다. 다음으로 추출된 특징을 학습하고 그 중 학습이 잘된 식별력 있는 특징을 선택하게 된다. 그 이후 식별력 있는 특징을 이용하여 얼굴이미지를 인식하게 된다. 얼굴인식을 위해 사용하는 얼굴이미지의 특징의 수는 매우 많다. 이 많은 특징을 학습 및 인식에 다 사용할 경우 학습 시간과 컴퓨팅 자원의 효율성이 떨어지는 문제점을 가지고 있다. 이러한 문제를 해결하기 위해서 최근 여러가지의 Boosting 기법이 소개되어왔다. Boosting 기법은 특징을 효율적으로 선택하여 학습 알고리즘의 성능을 좋게해주는 기법이다. 그 중 MutualBoost라는 기법이 있는데 이 기법은 특징간의 상호정보를 이용하여 특징을 효율적으로 선택하게 하는 기법이다. 본 논문에서는 MutualBoost의 효과를 더 증대시키기 위해서 개별적인 특징학습이 아니라 특징들을 Group화하여 특징학습을 하는 GroupMutual-Boost기법을 제안한다. 특징들을 Group화 함으로써 특징의 학습 및 선택 시간이 줄어들게 되고 컴퓨팅 자원을 보다 효율적으로 사용할 수 있다.

• 대한불안의학회지
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• 제7권1호
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• pp.3-8
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• 2011

Cognitive models of panic disorder have emphasized cognitive distortions' roles in the maintenance and treatment of panic disorder (PD). However, the patient's difficulty with identifying and managing emotional experiences might contribute to an enduring vulnerability to panic attacks. Numerous researchers, employing emotion processing paradigms and neuroimaging techniques, have investigated the empirical evidence for poor emotion processing in PD. For years, researchers considered that abnormal emotion processing in PD might reflect a dysfunction of the frontal-temporal-limbic circuits. Although neuropsychological studies have not provided consistent results regarding this model, a few studies have tried to find the biological basis of dysfunctional emotion processing in PD. In this article, we examine the possibility of dysregulation of emotion processing in PD. Specifically we discuss the neural basis of emotion processing and the manner in which such neurocognitive impairments may help clarify PD's core symptoms.

• International Journal of Human Ecology
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• 제4권2호
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• pp.89-100
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• 2003

This paper identifies trends in research methodology due to globalization. Context in both research and in practice and forms the key perspective for modern methodology and theory. Ecological perspectives are a necessary condition for quality global research. Human ecology researchers must advance the role of interdisciplinary and inter-functional perspectives and be open to collaborative relationships. These researchers must work in teams across disciplinary and functional boundaries. The paper discusses directions for research within the context of trends at U.S. federal agencies with applications to globalization and family life. Trends include： (a) use of diverse but rigorous methodologies； (b) recognition of the research-practice-research feedback loop；(c) primacy of context and diverse sampling; and (d) connections of research to problem solving. The terms promoted recently such as ″relationships″, ″diversity″ or ″problem-based″ are ingrained in human ecology. Key aspects for research in the next decade will be： (a) seeking diversity in sampling； (b) seeking colleagues with different perspectives； (c) incorporating meta-analysis into our work； (d) seeking meaningful results； (e) utilizing varieties of research methodologies to address our problems； and (0 understanding that practice must continually change as a function of research.

• Journal of the Korean Wood Science and Technology
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• 제20권3호
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• pp.61-66
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• 1992

It is well known that polymer chemistry started by the study on cellulose. However, the study on cellulose has not made a significant progress after the 2nd World War, because the interest of researchers has directed to the newly developed synthetic polymer science. Recently the situation has been changing as suggested by the creation of a word "Cellulose Renaissance". This change is due to the recognition that cellulose is a renewable resource and a biodegradable, environmentally friendly material. In this lecture I'd like to introduce you some recent topics in cellulose chemistry which were reported by Japanese researchers.