• Proceedings of the KIEE Conference
• /
• 2005.05a
• /
• pp.57-59
• /
• 2005

In our study, the automatic tracking algorithm of a human face is proposed by utilizing the thermal properties and 2nd momented geometrical feature of an infrared image. First, the facial candidates are estimated by restricting the certain range of thermal values, and the spurious blobs cleaning algorithm is applied to track the refined facial region in an infrared image.

• Proceedings of the KIEE Conference
• /
• 2005.05a
• /
• pp.63-65
• /
• 2005

In this study, the automatic face tracking algorithm is proposed by using the color and edge information of a color image. To reduce the effects of variations in the illumination conditions, an acquired CCD color image is first transformed into YCbCr color coordinates, and subsequently the morphological image processing operations, and the elliptical geometric measures are applied to extract the refined facial area.

• Journal of information and communication convergence engineering
• /
• v.3 no.2
• /
• pp.110-113
• /
• 2005

An enhancement of tracking capacity for the centering position of eye and iris on the facial image is presented. A facial image is acquisitioned with a CCD camera to be converted into a binary image. The eye region to be a specified brightness and shapes is used the FRM method using the neighboring five mask areas, and the iris on the eye is tracked with FPDP method. The experimental result shows that the proposed methods lead the centering position tracking capability to be enhanced than the pixel average coordinate values method.

• The KIPS Transactions:PartB
• /
• v.10B no.6
• /
• pp.711-718
• /
• 2003

In this paper, we propose a robust face tracking algorithm. It is based on Condensation algorithm [7] and uses skin color and facial shape as the observation measure. It is hard to integrate color weight and shape weight. So we propose the method that has two separate trackers which uses skin color and facial shape as the observation measure respectively. One tracker tracks skin colored region and the other tracks facial shape. We used importance sampling technique to limit sampling region of two trackers. For skin-colored region tracker, we propose an adaptive color model to avoid the effect of illumination change. The proposed face tracker performs robustly in clutter background and in the illumination changes.

• Proceedings of the KIEE Conference
• /
• 2005.05a
• /
• pp.60-62
• /
• 2005

In this study, the automatic tracking algorithm tracing a human face is proposed by using YCbCr color coordinated information and its thermal properties expressed in terms of thermal indexes in an infra-red image. The facial candidates are separately estimated in CbCr color and infra-red domain, respectively with applying the morphological image processing operations and the geometrical shape measures for fitting the elliptical features of a human face. The identification of a true face is accomplished by logical 'AND' operation between the refined image in CbCr color and infra-red domain.

• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.45 no.1
• /
• pp.75-83
• /
• 2008

We propose a welfare interface using multiple fecial features tracking, which can efficiently implement various mouse operations. The proposed system consist of five modules: face detection, eye detection, mouth detection, facial feature tracking, and mouse control. The facial region is first obtained using skin-color model and connected-component analysis(CCs). Thereafter the eye regions are localized using neutral network(NN)-based texture classifier that discriminates the facial region into eye class and non-eye class, and then mouth region is localized using edge detector. Once eye and mouth regions are localized they are continuously and correctly tracking by mean-shift algorithm and template matching, respectively. Based on the tracking results, mouse operations such as movement or click are implemented. To assess the validity of the proposed system, it was applied to the interface system for web browser and was tested on a group of 25 users. The results show that our system have the accuracy of 99% and process more than 21 frame/sec on PC for the $320{\times}240$ size input image, as such it can supply a user-friendly and convenient access to a computer in real-time operation.

• The Transactions of the Korean Institute of Electrical Engineers D
• /
• v.54 no.9
• /
• pp.577-579
• /
• 2005

In this study, the automatic tracking algorithm tracing a human face is proposed by using YCbCr color coordinated information and its thermal properties expressed in terms of thermal indexes in an infra-red image. The facial candidates are separately estimated in CbCr color and infra-red domain, respectively with applying the morphological image processing operations and the geometrical shape measures for fitting the elliptical features of a human face. The identification of a true face is accomplished by logical 'AND' operation between the refined image in CbCr color and infra-red domain.

• Journal of Korea Multimedia Society
• /
• v.18 no.5
• /
• pp.610-619
• /
• 2015

*In this paper, we propose a real-time face tracking system using an adaptive face detector and a tracking algorithm. An image is divided into the regions of background and face candidate by a real-time updated skin color identifying system in order to accurately detect facial features. The facial characteristics are extracted using the five types of simple Haar-like features. The extracted features are reinterpreted by Principal Component Analysis (PCA), and the interpreted principal components are processed by Support Vector Machine (SVM) that classifies into facial and non-facial areas. The movement of the face is traced by Kalman filter and Mean shift, which use the static information of the detected faces and the differences between previous and current frames. The proposed system identifies the initial skin color and updates it through a real-time color detecting system. A similar background color can be removed by updating the skin color. Also, the performance increases up to 20% when the background color is reduced in comparison to extracting features from the entire region. The increased detection rate and speed are acquired by the usage of Kalman filter and Mean shift.

• Journal of Institute of Control, Robotics and Systems
• /
• v.13 no.4
• /
• pp.371-376
• /
• 2007

This paper presents an emotion recognition and its expression system of an intelligent robot like a home robot or a service robot. Emotion recognition method in the robot is used by a facial image. We use a motion and a position of many facial features. apply a tracking algorithm to recognize a moving user in the mobile robot and eliminate a skin color of a hand and a background without a facial region by using the facial region detecting algorithm in objecting user image. After normalizer operations are the image enlarge or reduction by distance of the detecting facial region and the image revolution transformation by an angel of a face, the mobile robot can object the facial image of a fixing size. And materialize a multi feature selection algorithm to enable robot to recognize an emotion of user. In this paper, used a multi layer perceptron of Artificial Neural Network(ANN) as a pattern recognition art, and a Back Propagation(BP) algorithm as a learning algorithm. Emotion of user that robot recognized is expressed as a graphic LCD. At this time, change two coordinates as the number of times of emotion expressed in ANN, and change a parameter of facial elements(eyes, eyebrows, mouth) as the change of two coordinates. By materializing the system, expressed the complex emotion of human as the avatar of LCD.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2013.05a
• /
• pp.373-376
• /
• 2013

Nowadays, eyes detection is required and considered as the most important step in several applications, such as eye tracking, face identification and recognition, facial expression analysis and iris detection. This paper presents the eyes detection in facial images using Harris corner detection. Firstly, Haar-like features for face detection is used to detect a face region in an image. To separate the region of the eyes from a whole face region, the projection function is applied in this paper. At the last step, Harris corner detection is used to detect the eyes location. In experimental results, the eyes location on both grayscale and color facial images were detected accurately and effectively.