• The Journal of the Korea Contents Association
• /
• v.10 no.11
• /
• pp.20-29
• /
• 2010

The typical Kinetic Family Drawing (KFD) Web database system, a form of prototype system, has been developed, relying on the suggestions from family art therapists, with an aim to handle large amounts of assessment data and to facilitate effective implement of assessment activities. However, unfortunately such a system has an intrinsic problem that it fails to collect clients' behaviors, attitudes, facial expressions, voices, and other critical information observed while they are drawing. Accordingly we propose the ontology based video event analysis and video retrieval system in this paper, in order to enhance the function of a KFD Web database system by using a web camera and drawing tool. More specifically, a newly proposed system is designed to deliver two kinds of services: the client video retrieval service and the sketch video retrieval service, accompanied by a summary report of occurred events and dynamic behaviors relative to each family member object, respectively. The proposed system can support the reinforced KFD assessments by providing quantitative and subjective information on clients' working attitudes and behaviors, and KFD preparation processes.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2016.10a
• /
• pp.843-845
• /
• 2016

Concomitant with development of AR(Augmented Reality) and VR(Virtual Reality), interest in location based games and augmented reality have increased and various services have been developed. Due to this, AR, VR, MR(Merged Reality) techniques are considered one of fourth industrial revolution techniques. Oculus Rift which is representative of VR technology, and HTC VIVE need separate controllers, and don't offer service such as receiving external video information when users are using devices. In the case of project Alloy, which is a MR device of Intel, this has advantages such as controlling devices through hand signals and facial expressions, and receiving external video information. In this paper, we propose tourist guide system based on current position using MR. This system makes economical tour possible by arranging tourist attraction information which is from current position of tourists in correct places through external video information using MR, location determination using GPS, tourism information search using wireless internet.

• 제어로봇시스템학회:학술대회논문집
• /
• 2005.06a
• /
• pp.1689-1694
• /
• 2005

Face detection plays an important role in face recognition, video surveillance, and human computer interface. In this paper, we present a face detection system using eye detection with progressive thresholding from a digital camera. The face candidate is detected by using skin color segmentation in the YCbCr color space. The face candidates are verified by detecting the eyes that is located by iterative thresholding and correlation coefficients. Preprocessing includes histogram equalization, log transformation, and gray-scale morphology for the emphasized eyes image. The distance of the eye candidate points generated by the progressive increasing threshold value is employed to extract the facial region. The process of the face detection is repeated by using the increasing threshold value. Experimental results show that more enhanced face detection in real time.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.26 no.3B
• /
• pp.338-346
• /
• 2001

이 논문에서는 잡음에 대해 유연성이 있는 신경망과 차영상법-DCT를 이용한 얼굴인식 알고리즘을 제안한다. 동일환경(조도의 세기, 얼굴에서 카메라까지의 거리)에서 연속적으로 두 개의 영상을 캡쳐했다. 이 때 한 영상은 얼굴을 포함하지 않고 다른 영상은 얼굴을 포함하게 된다. 차영상 방법을 이용하여 두 개의 이미지로부터 얼굴영상과 배경영상을 분리하고 그 다움에 분리된 얼굴영역에서 사각영역을 추출하여 이 영역을 얼굴의 특징영역으로 이용하였다. 이 사각 영역은 눈, 코, 입, 눈썹 등이 포함된다. 다음으로 이 영역에 대해 DWT 연산을 수행한후 특징 백터를 추출하였고, 추출된 특징벡터는 정규화 되어 신경망의 입력벡터로 사용되었다. 시뮬레이션 결과 학습된 얼굴영상에 대해서는 100% 인식률을 보였고 학습되지 않는 얼굴 영상에 대해서는 92.25%의 인식률을 보였다.

• Proceedings of the Korean Information Science Society Conference
• /
• 2001.04b
• /
• pp.613-615
• /
• 2001

본 논문은 동영상 내의 얼굴을 특정인 얼굴로 자동 변환 및 정합하는 기술을 제안한다. 얼굴에 나타난 동작이나 표정은 높은 자유도로 인하여 기존에 사용되어온 2차원적이고 고정된 물체 위주의 동영상 정합 기술로는 자연스러운 결과물을 얻기가 어렵다. 본 논문에서는 입력 받은 정면 유사방향의 사진으로부터 3차원 얼굴 모델을 복원한다. 각 프레임에 등장한 얼굴의 3차원 방향을 추출하여 복원한 3차원 얼굴 모델에 적용한 후 대체할 얼굴 영역에 저합시킨다. 정합 과정 시 비디오 프레임 내의 조명효과와 얼굴색 등을 분석하고 3차원 얼굴 모델에 블렌딩하여 비디오 프레임과 자연스럽게 정합할 수 있도록 한다.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.6 no.9
• /
• pp.2341-2350
• /
• 2012

Face detection is the first step of vision-based driver fatigue detection method. Traditional face detection methods have problems of high false-detection rates and long detection times. A space-time restrained Adaboost method is presented in this paper that resolves these problems. Firstly, the possible position of a driver's face in a video frame is measured relative to the previous frame. Secondly, a space-time restriction strategy is designed to restrain the detection window and scale of the Adaboost method to reduce time consumption and false-detection of face detection. Finally, a face knowledge restriction strategy is designed to confirm that the faces detected by this Adaboost method. Experiments compare the methods and confirm that a driver's face can be detected rapidly and precisely.

• Journal of the Semiconductor & Display Technology
• /
• v.19 no.2
• /
• pp.100-104
• /
• 2020

Over the past several years, deep learning has been widely used for feature extraction in image and video for various applications such as object classification and facial recognition. This paper introduces an implantation of embedded Linux system for embossed digits recognition using CNN based deep learning methods. For this purpose, we implemented a coin recognition system based on deep learning with the Keras open source library on Raspberry PI. The performance evaluation has been made with the success rate of coin classification using the images captured with ultra-wide angle camera on Raspberry PI. The simulation result shows 98% of the success rate on average.

• Journal of IKEEE
• /
• v.22 no.4
• /
• pp.992-998
• /
• 2018

Presentation is a technique that is logically and confidently conveying your thoughts and opinions in front of others. It is essential when you go to school or work. However, it takes a lot of time, money, and effort to improve your presentation skills. In this paper, we describe the development of a presentation coaching application that analyzes the presentation practice video. The application program can analyze the presentation time, the speaker's expression, the use of duplicate words, etc.

• Journal of the Semiconductor & Display Technology
• /
• v.17 no.4
• /
• pp.105-108
• /
• 2018

Face Detection is an essential first step of the face recognition, and this is significant effects on face feature extraction and the effects of face recognition. Face detection has extensive research value and significance. In this paper, we present and analysis the principle, merits and demerits of the classic AdaBoost face detection and ASM algorithm based on point distribution model, which ASM solves the problems of face detection based on AdaBoost. First, the implemented scheme uses AdaBoost algorithm to detect original face from input images or video stream. Then, it uses ASM algorithm converges, which fit face region detected by AdaBoost to detect faces more accurately. Finally, it cuts out the specified size of the facial region on the basis of the positioning coordinates of eyes. The experimental result shows that the method can detect face rapidly and precisely, with a strong robustness.

• Proceedings of the Korean Society of Broadcast Engineers Conference
• /
• 2019.06a
• /
• pp.35-37
• /
• 2019

최근 딥 러닝의 급격한 발전과 함께 얼굴표정인식 기술이 상당한 진보를 이루었다. 그러나 기존 얼굴표정인식 기법들은 제한된 환경에서 취득한 인위적인 동영상에 대해 주로 개발되었기 때문에 실제 wild 한 환경에서 취득한 동영상에 대해 강인하게 동작하지 않을 수 있다. 이런 문제를 해결하기 위해 3D CNN, 2D CNN 그리고 RNN 의 새로운 결합으로 이루어진 Deep neural network 구조를 제안한다. 제안 네트워크는 주어진 동영상으로부터 두 가지 서로 다른 CNN 을 통해서 영상 내 공간적 정보뿐만 아니라 시간적 정보를 담고 있는 특징 벡터를 추출할 수 있다. 그 다음, RNN 이 시간 도메인 학습을 수행할 뿐만 아니라 상기 네트워크들에서 추출된 특징 벡터들을 융합한다. 상기 기술들이 유기적으로 연동하는 제안된 네트워크는 대표적인 wild 한 공인 데이터세트인 AFEW 로 실험한 결과 49.6%의 정확도로 종래 기법 대비 향상된 성능을 보인다.