• Journal of the Institute of Electronics Engineers of Korea CI
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• v.47 no.3
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• pp.1-10
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• 2010

As various interfacing devices for computational machines are being developed, a new HCI method using hand motion input is introduced. This interface method is a vision-based approach using a single camera for detecting and tracking hand movements. In the previous researches, only a skin color is used for detecting and tracking hand location. However, in our design, skin color and shape information are collectively considered. Consequently, detection ability of a hand increased. we proposed primary orientation edge descriptor for getting an edge information. This method uses only one hand model. Therefore, we do not need training processing time. This system consists of a detecting part and a tracking part for efficient processing. In tracking part, the system is quite robust on the orientation of the hand. The system is applied to recognize a hand written number in script style using DNAC algorithm. Performance of the proposed algorithm reaches 82% recognition ratio in detecting hand region and 90% in recognizing a written number in script style.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.4 no.3
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• pp.202-208
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• 2011

Recently the technologies that control the computer system through human computer interaction(HCI) have been widely studied. Their applications usually involve the methods that locate user's positions via face detection and recognize user's gestures, but face detection performance is not good enough. In case that the applications do not locate user's position stably, user interface performance, such as gesture recognition, is significantly decreased. In this paper we propose a new stable face detection algorithm using skin color detection and cumulative distribution of face detection results, whose effectiveness was verified by experiments. The propsed algorithm can be applicable in the area of human tracking that uses correspondence matrix analysis.

• Journal of the Korea Society of Computer and Information
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• v.15 no.3
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• pp.27-35
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• 2010

Accurately extracting skin color regions is very important in various areas such as face recognition and tracking, facial expression recognition, adult image identification, health-care, and so forth. In this paper, we evaluate the performances of several skin color detection algorithms in indoor environments by changing the distance between the camera and the object as well as the background colors of the object. The distance is from 60cm to 120cm and the background colors are white, black, orange, pink, and yellow, respectively. The algorithms that we use for the performance evaluation are Peer algorithm, NNYUV, NNHSV, LutYUV, and Kimset algorithm. The experimental results show that NNHSV, NNYUV and LutYUV algorithm are stable, but the other algorithms are somewhat sensitive to the changes of backgrounds. As a result, we expect that the comparative experimental results of this paper will be used very effectively when developing a new skin color extraction algorithm which are very robust to dynamic real environments.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.9
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• pp.1825-1838
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• 2012

This paper proposes a fast face detection and tracking method which uses depth images as well as RGB images. It consists of the face detection procedure and the face tracking procedure. The face detection method basically uses an existing method, Adaboost, but it reduces the size of the search area by using the depth information and skin color. The proposed face tracking method uses a template matching technique and incorporates an early-termination scheme to reduce the execution time further. The results from implementing and experimenting the proposed methods showed that the proposed face detection method takes only about 39% of the execution time of the existing method. The proposed tracking method takes only 2.48ms per frame. For the exactness, the proposed detection method and previous method showed a same detection ratio but in the error ratio, which is about 0.66%, the proposed method showed considerably improved performance. In all the cases except a special one, the tracking error ratio is as low as about 1%. Therefore, we expect the proposed face detection and tracking methods can be used individually or in combined for many applications that need fast execution and exact detection or tracking.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.3
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• pp.229-235
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• 2011

To make a robust object tracking and identifying system for an intelligent robot and/or home system, heterogeneous sensor fusion between visible ray system and infrared ray system is proposed. The proposed system separates the object by combining the ROI (Region of Interest) estimated from two different images based on a heterogeneous sensor that consolidates the ordinary CCD camera and the IR (Infrared) camera. Human's body and face are detected in both images by using different algorithms, such as histogram, optical-flow, skin-color model and Haar model. Also the pose of human body is estimated from the result of body detection in IR image by using PCA algorithm along with AdaBoost algorithm. Then, the results from each detection algorithm are fused to extract the best detection result. To verify the heterogeneous sensor fusion system, few experiments were done in various environments. From the experimental results, the system seems to have good tracking and identification performance regardless of the environmental changes. The application area of the proposed system is not limited to robot or home system but the surveillance system and military system.

• The Journal of the Korea institute of electronic communication sciences
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• v.5 no.3
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• pp.309-314
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• 2010

In this paper, an intelligent security camera over internet is proposed. Among ISC methods, watersheds based methods produce a good performance in segmentation accuracy. But traditional watershed transform has been suffered from over-segmentation due to small local minima included in gradient image that is input to the watershed transform. And a zone face candidates of detection using skin-color model. last step, face to check at face of candidate location using SVM method. It is extract of wavelet transform coefficient to the zone face candidated. Therefore, it is likely that it is applicable to read world problem, such as object tracking, surveillance, and human computer interface application etc.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.47 no.5
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• pp.1-9
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• 2010

Generally, dynamic hand gesture recognition is formed through preprocessing step, hand tracking step and hand shape detection step. In this paper, we present advanced dynamic hand gesture recognizing method that improves performance in preprocessing step and hand shape detection step. In preprocessing step, we remove noise fast by using dynamic table and detect skin color exactly on complex background for controling skin color range in skin color detection method using YCbCr color space. Especially, we increase recognizing speed in hand shape detection step through detecting Start Image and Stop Image, that are elements of dynamic hand gesture recognizing, using Guideline. Guideline is edge of input hand image and hand shape for comparing. We perform various experiments with nine web-cam video clips that are separated to complex background and simple background for dynamic hand gesture recognition method in the paper. The result of experiment shows similar recognition ratio but high recognition speed, low cpu usage, low memory usage than recognition method using learning exercise.

• The Journal of Korea Robotics Society
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• v.10 no.3
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• pp.171-177
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• 2015

A telerobot offers a more engaging and enjoyable interaction with people at a distance by communicating via audio, video, expressive gestures, body pose and proxemics. To provide its potential benefits at a reasonable cost, this paper presents a telepresence robot system for video communication which can deliver speaker's head motion through its display stanchion. Head gestures such as nodding and head-shaking can give crucial information during conversation. We also can assume a speaker's eye-gaze, which is known as one of the key non-verbal signals for interaction, from his/her head pose. In order to develop an efficient head tracking method, a 3D cylinder-like head model is employed and the Harris corner detector is combined with the Lucas-Kanade optical flow that is known to be suitable for extracting 3D motion information of the model. Especially, a skin color-based face detection algorithm is proposed to achieve robust performance upon variant directions while maintaining reasonable computational cost. The performance of the proposed head tracking algorithm is verified through the experiments using BU's standard data sets. A design of robot platform is also described as well as the design of supporting systems such as video transmission and robot control interfaces.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.38 no.6
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• pp.684-691
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• 2001

In this paper, we design and implement a system which recognizes and tracks a human head on a sequence of images. In this paper, the color of the skin and ellipse modeling is used as feature vectors to recognize the human head. And the modified time-varying edge detection method and the vertical projection method is used to acquire regions of the motion from images with very complex backgrounds. To select the head from the acquired candidate regions, the process for thresholding on the basis of the I-component of YIQ color information and mapping with ellipse modeling is used. The designed system shows an excellent performance in the cases of the rotated heads, occluded heads, and tilted heads as well as in the case of the normal up-right heads. And in this paper, the combinational technique of motion-based tracking and recognition-based tracking is used to track the human head exactly even though the human head moves fast.

• Proceedings of the KIEE Conference
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• 2006.10c
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• pp.214-216
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• 2006

Expressive face and human body gestures are among the main non-verbal communication channels in human-human interaction. Understanding human emotions through body gesture is one of the necessary skills both for humans and also for the computers to interact with their human counterparts. Gesture analysis is consisted of several processes such as detecting of hand, extracting feature, and recognizing emotions. Skin color information for tracking hand gesture is obtained from face detection region. We have revealed relationships between paricular body movements and specific emotions by using HMM(Hidden Markov Model) classifier. Performance evaluation of emotional human body recognition has experimented.