• Journal of Korea Multimedia Society
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• v.14 no.4
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• pp.481-493
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• 2011

As the development of mobile devices, mobile phones are equipped with many features. Video-call feature is one of them. In this paper, we present distance measurement between speaker and smart-phone using multiple color spaces and multiple thresholds. first, detect face based on skin color information. and second, measure distance between speaker and smart-phone using the detected face region. Especially, the first considering point in the development of face area detection is real-time processing and the second point is robustness to solve the problems of face detection errors due to rapid change of object movement, lighting and background between adjacent frames.

• Journal of the Semiconductor & Display Technology
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• v.4 no.1 s.10
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• pp.61-64
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• 2005

This study presents a new algorithm to detect the facial feature in a color image entered from the mobile device with complex backgrounds and undefined distance between camera's location and the face. Since skin color model with Hough transformation spent approximately 90$\%$ of running time to extract the fitting ellipse for detection of the facial feature, we have changed the approach to the simple geometric vector operation, called a TS(Triangle-Square) transformation. As the experimental results, this gives benefit of reduced run time. We have similar ratio of face detection to other methods with fast speed enough to be used on real-time identification system in mobile environments.

• Proceedings of the IEEK Conference
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• 2002.07a
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• pp.615-618
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• 2002

This paper proposes a real-time face detection and tracking a method in complex backgrounds. The proposed method is based on the principal component analysis (PCA) technique. For the detection of a face, first, we use a skin color model and motion information. And then using the PCA technique the detected regions are verified to determine which region is indeed the face. The tracking of a face is based on the Euclidian distance in eigenspace between the previously tracked face and the newly detected faces. Camera control for the face tracking is done in such a way that the detected face region is kept on the center of the screen by controlling the pan/tilt platform. The proposed method is extensible to other systems such as teleconferencing system, intruder inspection system, and so on.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2003.05a
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• pp.155-160
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• 2003

This paper presents a multiple face detector based on a robust pupil detection technique. The pupil detector uses active illumination that exploits the retro-reflectivity property of eyes to facilitate detection. The detection range of this method is appropriate for interactive desktop and kiosk applications. Once the location of the pupil candidates are computed, the candidates are filtered and grouped into pairs that correspond to faces using heuristic rules. To demonstrate the robustness of the face detection technique, a dual mode face tracker was developed, which is initialized with the most salient detected face. Recursive estimators are used to guarantee the stability of the process and combine the measurements from the multi-face detector and a feature correlation tracker. The estimated position of the face is used to control a pan-tilt servo mechanism in real-time, that moves the camera to keep the tracked face always centered in the image.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.41 no.6
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• pp.61-71
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• 2004

Recently, various techniques for face detection are studied, but most of them still have problems on processing in real-time. Therefore, in this paper, we propose novel techniques for real-time detection of human faces in sequential images using motion and chroma information. First, background model is used to find a moving area. In this procmoving area. edure, intensity values for reference images are averaged, then skin-color are detected in We use HCr color-space model and adaptive threshold method for detection. Second, binary image labeling is applied to acquire candidate regions for faces. Candidates for mouth and eyes on a face are obtained using differences between green(G) and blue(B), intensity(I) and chroma-red(Cr) value. We also considered distances between eye points and mouth on a face. Experimental results show effectiveness of real-time detection for human faces in sequential images.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.8 no.3
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• pp.727-732
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• 2004

In this paper, we introduce a new approach for real-time 3D face pose discrimination based on active IR illumination from a monocular view of the camera. Under the IR illumination, the pupils appear bright. We develop algorithms for efficient and robust detection and tracking pupils in real time. Based on the geometric distortions of pupils under different face orientations, an eigen eye feature space is built based on training data that captures the relationship between 3D face orientation and the geometric features of the pupils. The 3D face pose for an input query image is subsequently classified using the eigen eye feature space. From the experiment, we obtained the range of results of discrimination from the subjects which close to the camera are from 94,67％, minimum from 100％, maximum.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.47 no.2
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• pp.33-44
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• 2010

This paper propose the structure of real-time face detection hardware architecture for robot vision processing applications. The proposed architecture is robust against illumination changes and operates at no less than 60 frames per second. It uses Modified Census Transform to obtain face characteristics robust against illumination changes. And the AdaBoost algorithm is adopted to learn and generate the characteristics of the face data, and finally detected the face using this data. This paper describes the face detection hardware structure composed of Memory Interface, Image Scaler, MCT Generator, Candidate Detector, Confidence Comparator, Position Resizer, Data Grouper, and Detected Result Display, and verification Result of Hardware Implementation with using Virtex5 LX330 FPGA of Xilinx. Verification result with using the images from a camera showed that maximum 32 faces per one frame can be detected at the speed of maximum 149 frame per second.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.835-838
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• 2003

Computer vision and natural-language dialogue play an important role in friendly human-machine interfaces for service robots. In this paper we describe an integrated face detection and face recognition system for a welfare robot, which has also been combined with the robot's speech interface. Our approach to face detection is to combine neural network (NN) and genetic algorithm (GA): ANN serves as a face filter while GA is used to search the image efficiently. When the face is detected, embedded Hidden Markov Model (EMM) is used to determine its identity. A real-time system has been created by combining the face detection and recognition techniques. When motivated by the speaker's voice commands, it takes an image from the camera, finds the face inside the image and recognizes it. Experiments on an indoor environment with complex backgrounds showed that a recognition rate of more than 88% can be achieved.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.28.1-28
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• 2001

An automatic face recognition(AFR) of individuals is a significant problem in the development of computer vision. An AFR consists of two major parts which are detection of face region and recognition process, and the overall performance of AFR is determined by each. In this paper, the face region is acquired using chromaticity space, but this face region is a simple rectangle which doesn´t consider the shape information. By applying deformable templates to the face region, we can locate the position of the eyes in images. With the face region and the eye location information, more precise face region can be extract from the image. Because processing time is critical in real-time system, we use simplified eye templates and the modified energy function for the efficiency. We can get a good detection performance in experiments.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.783-788
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• 2003

In this paper, we present a new approach to detect and recognize human face in the image from vision camera equipped on the mobile robot platform. Due to the mobility of camera platform, obtained facial image is small and pose-various. For this condition, new algorithm should cope with these constraints and can detect and recognize face in nearly real time. In detection step, ‘coarse to fine’ detection strategy is used. Firstly, region boundary including face is roughly located by dual ellipse templates of facial color and on this region, the locations of three main facial features- two eyes and mouth-are estimated. For this, simplified facial feature maps using characteristic chrominance are made out and candidate pixels are segmented as eye or mouth pixels group. These candidate facial features are verified whether the length and orientation of feature pairs are suitable for face geometry. In recognition step, pseudo-convex hull area of gray face image is defined which area includes feature triangle connecting two eyes and mouth. And random lattice line set are composed and laid on this convex hull area, and then 2D appearance of this area is represented. From these procedures, facial information of detected face is obtained and face DB images are similarly processed for each person class. Based on facial information of these areas, distance measure of match of lattice lines is calculated and face image is recognized using this measure as a classifier. This proposed detection and recognition algorithms overcome the constraints of previous approach [15], make real-time face detection and recognition possible, and guarantee the correct recognition irregardless of some pose variation of face. The usefulness at mobile robot application is demonstrated.