• Proceedings of the Korean Information Science Society Conference
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• 2003.04c
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• pp.337-339
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• 2003

얼굴인식은 얼굴 요소간의 지형적 특징보다 얼굴 영역 영상을 그대로 사용하여 인식하는 외관기반(appearance-based) 방법이 선호된다. 이때, 배경의 영향을 배제하기 위해 얼굴요소정보를 포함하는 최소 사각 영역을 사용하거나, 타원 마스크를 적용한다. 그러나, 이러한 전처리 방법은 개인별 외관특징으로써의 얼굴 윤곽 정보를 활용하지 못하게 한다. 본 논문에는 얼굴의 윤곽정보를 추출하기 위한 전처리 절차를 제안하고, ORL 얼굴 데이터에 대한 인식률 실험을 통해 제안하는 방법이 얼굴인식 성능을 크게 향상시킬 수 있음을 보인다.

• Journal of Korea Multimedia Society
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• v.7 no.8
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• pp.1035-1045
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• 2004

In this paper, we describe a method that can recognize gestures and evaluate the degree of the gestures from sequential gesture images by using Gesture Feature Space. The previous popular methods based on HMM and neural network have difficulties in recognizing the degree of gesture even though it can classify gesture into some kinds. However, our proposed method can recognize not only posture but also the degree information of the gestures, such as speed and magnitude by calculating distance among the position vectors substituting input and model images in parametric eigenspace. This method which can be applied in various applications such as intelligent interface systems and surveillance systems is a simple and robust recognition algorithm.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.05a
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• pp.521-524
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• 2013

In this paper, the algorithm that recognizes the gesture by configuring the feature information obtained through Improved Higher Order Local Correlation Features as low dimensional gesture symbol was described. Since the proposed method doesn't require a lot of computations compared to the existing geometric feature based method or appearance based methods and it can maintain high recognition rate by using the minimum information, it is very well suited for real-time system establishment.

• Journal of KIISE:Software and Applications
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• v.32 no.8
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• pp.759-768
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• 2005

This paper describes an algorithm that can recognize gestures constructing subspace gesture symbols with hybrid feature information. The previous popular methods based on geometric feature and appearance have resulted in ambiguous output in case of recognizing between similar gesture because they use just the Position information of the hands, feet or bodily shape features. However, our proposed method can classify not only recognition of motion but also similar gestures by the partial feature information presenting which parts of body move and the global feature information including 2-dimensional bodily motion. And this method which is a simple and robust recognition algorithm can be applied in various application such surveillance system and intelligent interface systems.

• Annual Conference of KIPS
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• 2022.11a
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• pp.615-617
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• 2022

본 논문에서는 얼굴사진 기반 감정인식 심층망, 음성사운드를 기반한 감정인식 심층망을 결합한 앙상블 네트워크 구축을 위한 사전연구로서 얼굴사진 기반 감정을 인식하는 기존 딥뉴럴 네트워크 모델들을 입력 데이터 처리 방법에 따라 분류하고, 각 방법의 특성을 분석한다. 또한, 얼굴사진 외관 특성을 기반한 감정인식 네트워크를 여러 구조로 구성하고, 구성된 방법의 성능을 비교하여, 우수 성능을 보이는 네트워크를 선정하여 추후 앙상블 네트워크의 구성 네트워크로 사용하고자 한다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.10
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• pp.2121-2128
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• 2012

This paper proposed the real time face area detection using Wavelet transform and the strong detection algorithm that satisfies the efficiency of computation and detection performance at the same time was proposed. The detected face image recognizes the face by configuring the low-dimensional face symbol through the principal component analysis. The proposed method is well suited for real-time system construction because it doesn't require a lot of computation compared to the existing geometric feature-based method or appearance-based method and it can maintain high recognition rate using the minimum amount of information. In addition, in order to reduce the wrong recognition or recognition error occurred during face recognition, the input symbol of Hidden Markov Model is used by configuring the feature values projected to the unique space as a certain symbol through clustering algorithm. By doing so, any input face will be recognized as a face model that has the highest probability. As a result of experiment, when comparing the existing method Euclidean and Mahananobis, the proposed method showed superior recognition performance in incorrect matching or matching error.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.3
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• pp.341-348
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• 2020

The ability to extract useful information from an image, such as the human eye, is an interface technology essential for AI computer implementation. The building recognition technology has a lower recognition rate than other image recognition technologies due to the various building shapes, the ambient noise images according to the season, and the distortion by angle and distance. The computer vision based building recognition algorithms presented so far has limitations in discernment and expandability due to manual definition of building characteristics. This paper introduces the deep learning CNN (Convolutional Neural Network) model, and proposes new method to improve the recognition rate even by changes of building images caused by season, illumination, angle and perspective. This paper introduces the partial images that characterize the building, such as windows or wall images, and executes the training with whole building images. Experimental results show that the building recognition rate is improved by about 14% compared to the general CNN model.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.12
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• pp.2647-2654
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• 2011

In this paper, the algorithm that recognizes the gesture by configuring the feature information obtained through edge orientation histogram and principal component analysis as low dimensional gesture symbol was described. Since the proposed method doesn't require a lot of computations compared to the existing geometric feature based method or appearance based methods and it can maintain high recognition rate by using the minimum information, it is very well suited for real-time system establishment. In addition, to reduce incorrect recognition or recognition errors that occur during gesture recognition, the model feature values projected in the gesture space is configured as a particular status symbol through clustering algorithm to be used as input symbol of hidden Markov models. By doing so, any input gesture will be recognized as the corresponding gesture model with highest probability.

• 한국HCI학회:학술대회논문집
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• 2006.02a
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• pp.653-659
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• 2006

다중 객체 추적이란 컴퓨터 비전의 한 분야로, 주어진 비디오 시퀀스 내에서 관심 있는 객체들을 추적하는 것을 말한다. 다중 객체 추적 시스템은 감시 시스템, 사용자 행동 인식, 스포츠 중계, 비디오 회의와 같은 다양한 응용 분야에 핵심 기반 기술로 쓰이고 있어 그 중요성이 매우 크다. 본 논문은 감시 목적의 다중 객체를 추적하는 방법에 대하여 다룬다. 감시 시스템의 특성상, 객체의 외관이나 움직임 등에 대한 가정을 하기가 어렵다. 따라서 본 논문에서는 크기, 색, 형태 같은 객체의 단순하고 직관적인 외관 특성을 이용하면서도, 객체들끼리 부분적으로 혹은 완전히 겹쳐졌을 때에도 객체들의 위치를 적절히 추적할 수 있는 방법을 제안한다. 본 논문에서 제안하는 방법은 객체들의 경로에 대한 정보를 유지하는데 그래프 구조를 이용한다. 그래프를 확장하고, 제거하여 영상에 대한 정보를 추론한다. 크게 보면 객체들을 영역 레벨, 객체 레벨 두 단계에 걸쳐 추적한다. 영역 레벨에서는 각 객체들이 있을 수 있을만한 영역에 대한 가설을 세우고, 객체 레벨에서는 각 가설에 대한 검증을 한다. 제안된 방법은 직관적인 정보만을 이용하여 서로 다른 형태의 객체를 빠르게 추적할 수 있음을 보여준다. 다만 객체의 외관 정보만을 이용하였기 추적하기 때문에, 객체가 다른 객체에 의해 완전히 가려진 채 또다시 다른 객체와 겹쳐지면, 정확한 추적이 되지 않는다. 이를 해결하기 위해서는 객체가 겹쳐졌을 때, 그 관계에 대한 정보를 모아야 하는데 이는 향후 연구를 통해 해결하고자 한다.

• Journal of KIISE:Software and Applications
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• v.31 no.1
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• pp.54-60
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• 2004

Face recognition is one of the problems to be solved by appearance based matching technique. However, the appearance of face image is very sensitive to variation in illumination. One of the easiest ways for better performance is to collect more training samples acquired under variable lightings but it is not practical in real world. ]:n object recognition, it is desirable to focus on feature extraction or normalization technique rather than focus on classifier. This paper presents a simple approach to normalization of faces subject to directional illumination. This is one of the significant issues that cause error in the face recognition process. The proposed method, ICR(illumination Compensation based on Multiple Linear Regression), is to find the plane that best fits the intensity distribution of the face image using the multiple linear regression, then use this plane to normalize the face image. The advantages of our method are simple and practical. The planar approximation of a face image is mathematically defined by the simple linear model. We provide experimental results to demonstrate the performance of the proposed ICR method on public face databases and our database. The experimental results show a significant improvement of the recognition accuracy.