• Journal of KIISE:Software and Applications
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• v.36 no.2
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• pp.161-168
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• 2009

In this paper, we develop an Intelligent Wheelchair(IW) control system for the people with various disabilities. The aim of the proposed system is to increase the mobility of severely handicapped people by providing an adaptable and effective interface for a power wheelchair. To facilitate a wide variety of user abilities, the proposed system involves the use of face-inclination and mouth-shape information, where the direction of an Intelligent Wheelchair(IW) is determined by the inclination of the user's face, while proceeding and stopping are determined by the shape of the user's mouth. To analyze these gestures, our system consists of facial feature detector, facial feature recognizer, and converter. In the stage of facial feature detector, the facial region of the intended user is first obtained using Adaboost, thereafter the mouth region detected based on edge information. The extracted features are sent to the facial feature recognizer, which recognize the face inclination and mouth shape using statistical analysis and K-means clustering, respectively. These recognition results are then delivered to a converter to control the wheelchair. When assessing the effectiveness of the proposed system with 34 users unable to utilize a standard joystick, the results showed that the proposed system provided a friendly and convenient interface.

• Journal of the Institute of Convergence Signal Processing
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• v.6 no.1
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• pp.15-22
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• 2005

In this paper, we propose a face recognition system by using the CCD color image. We first get the face candidate image by using YCbCr color model and adaptive skin color information. And we use it initial curve of active contour model to extract face region. We use the Eye map and mouth map using color information for extracting facial feature from the face image. To obtain center point of Log-polar image, we use extracted facial feature from the face image. In order to obtain feature vectors, we use extracted coefficients from DCT and wavelet transform. To show the validity of the proposed method, we performed a face recognition using neural network with BP learning algorithm. Experimental results show that the proposed method is robuster with higher recogntion rate than the conventional method for the rotation and scale variant.

• The Journal of the Korea institute of electronic communication sciences
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• v.10 no.11
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• pp.1307-1314
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• 2015

This study proposed a more stable robust recognition algorithm which detects faces reliably even in cases where there are changes in lighting and angle of view, as well it satisfies efficiency in calculation and detection performance. The algorithm proposed detects the face area alone after normalization through pre-processing and obtains a feature vector using (PCA). Also, by applying the feature vector obtained for SVM, face areas can be tested. After the testing, the feature vector is applied to LDA and using Euclidean distance in the 2nd dimension, the final analysis and matching is performed. The algorithm proposed in this study could increase the stability and accuracy of recognition rates and as a large amount of calculation was not necessary due to the use of two dimensions, real-time recognition was possible.

• Smart Media Journal
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• v.5 no.1
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• pp.38-43
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• 2016

As smart environment is spread out in our living environments, the needs of an approach related to Human Computer Interaction(HCI) is increases. One of them is head pose estimation. it related to gaze direction estimation, since head has a close relationship to eyes by the body structure. It's a key factor in identifying person's intention or the target of interest, hence it is an essential research in HCI. In this paper, we propose an approach for head pose estimation with pre-defined several directions by random forest classifier. We use canny edge detector to extract feature of the different facial image which is obtained between input image and averaged frontal facial image for extraction of rotation information of input image. From that, we obtain the binary edge image, and make two accumulated histograms which are obtained by counting the number of pixel which has non-zero value along each of the axes. This two accumulated histograms are used to feature of the facial image. We use CAS-PEAL-R1 Dataset for training and testing to random forest classifier, and obtained 80.6% accuracy.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.10 no.6
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• pp.609-616
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• 2017

In this paper, we propose an algorithm to obtain more stable and high recognition rate by using left and right rotation information of input image in order to obtain a stable recognition rate in face recognition. The proposed algorithm uses the facial image as the input information in the web camera environment to reduce the size of the image and normalize the information about the brightness and color to obtain the improved recognition rate. We apply Principal Component Analysis (PCA) to the detected candidate regions to obtain feature vectors and classify faces. Also, In order to reduce the error rate range of the recognition rate, a set of data with the left and right $45^{\circ}$ rotation information is constructed considering the directionality of the input face image, and each feature vector is obtained with PCA. In order to obtain a stable recognition rate with the obtained feature vector, it is after scattered in the eigenspace and the final face is recognized by comparing euclidean distant distances to each feature. The PCA-based feature vector is low-dimensional data, but there is no problem in expressing the face, and the recognition speed can be fast because of the small amount of calculation. The method proposed in this paper can improve the safety and accuracy of recognition and recognition rate faster than other algorithms, and can be used for real-time recognition system.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.36S no.12
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• pp.107-119
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• 1999

There is no authentic solution in a face detection problem though it is an important part of pattern recognition and has many diverse application fields. The reason is that there are many unpredictable deformations due to facial expressions, view point, rotation, scale, gender, age, etc. To overcome these problems, we propose an algorithm based on feature-based method, which is well known to be robust to these deformations. We detect a face by calculating a similarity between the formation of real face feature and candidate feature formation which consists of eyebrow, eye, nose, and mouth. In this paper, we use a steerable filter instead of general derivative edge detector in order to get more accurate feature components. We applied deformable template to verify the detected face, which overcome the weak point of feature-based method. Considering the low detection rate because of face detection method using whole input images, we design an adaptive skin-color filter which can be applicable to a diverse skin color, minimizing target area and processing time.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.43 no.4 s.310
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• pp.58-66
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• 2006

In this paper, we apply DCM(Dilation of Color and Motion information) mask and Active Contour Models(Snakes) to extract facial outline in moving pictures with complex background. First, we propose DCM mask which is made by applying morphology dilation and AND operation to combine facial color and motion information, and use this mask to detect facial region without complex background and to remove noise in image energy. Also, initial curves are automatically set according to rotational degree estimated with geometric ratio of facial elements to overcome the demerit of Active Contour Models which is sensitive to initial curves. And edge intensity and brightness are both used as image energy of snakes to extract contour at parts with weak edges. For experiments, we acquired total 480 frames with various head-poses of sixteen persons with both eyes shown by taking pictures in inner space and also by capturing broadcasting images. As a result, it showed that more elaborate facial contour is extracted at average processing time of 0.28 seconds when using interpolated initial curves according to facial rotation degree and using combined image energy of edge intensity and brightness.

• Journal of KIISE:Software and Applications
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• v.30 no.3_4
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• pp.372-378
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• 2003

Limitations on the linear discriminant analysis (LDA) for face rerognition, such as the loss of generalization and the computational infeasibility, are addressed and illustrated for a small number of samples. The principal component analysis (PCA) followed by the LDA mapping may be an alternative that ran overcome these limitations. We also show that any schemes based on either mappings or template matching are vulnerable to image variations due to rotation, translation, facial expressions, or local illumination conditions. This entails the importance of a proper preprocessing that can compensate for such variations. A simple template matching, when combined with the geometrically correlated feature-based detection as a preprocessing, is shown to outperform mapping techniques in terms of both the accuracy and the robustness to image variations.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2012.11a
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• pp.151-154
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• 2012

본 논문에서는 화상통신의 현실감을 증진시킬 수 있는 화자 간 시선 맞춤 시스템을 제안한다. 제안하는 방법은 Kinect 거리 카메라로부터 입력된 영상에서 화자의 얼굴 영역을 획득하여 화자의 시선이 카메라를 응시하도록 획득한 영역을 변환한 후에 원본 영상과 합성한다. Kinect 거리 카메라에서 획득한 얼굴 영역에는 다양한 형태의 잡음이 많아 미디언 필터와 모폴로지 연산을 통해 얼굴 영역의 잡음을 제거한다. 화자의 위치에 상관 없이 화자가 카메라를 응시하는 영상을 생성하기 위해서 Kinect 가 제공하는 거리 정보를 이용하여 시선 보정 각도와 회전 축을 획득한다. 시선이 보정된 얼굴 영역은 원본 영상에서 존재하지 않는 영역을 포함하고 있기 때문에, 원본 영상의 각 화소를 삼각형 메쉬로 구성한 후 해당 영역을 보간하여 최종적으로 시선이 보정된 영상을 생성한다. 제안하는 방법은 시선 맞춤 영상을 생성하는 데 필수적인 눈과 주변 얼굴 영역만 선택해서 변환하므로 영상의 왜곡이 적고 실시간 처리가 가능하다는 장점이 있다. 또한 카메라와 화자 사이의 거리 정보를 이용해 화자의 위치에 적응적인 시선 맞춤 영상을 생성할 수 있다. 실험을 통해 Intel i5 CPU 를 장착한 PC에서 $320{\times}240$ 크기의 영상을 사용할 경우 초당 약 35 프레임의 보정된 영상을 생성하여 제안하는 방법이 실시간 처리가 가능하다는 것을 확인하였다.

• Journal of Korea Multimedia Society
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• v.15 no.1
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• pp.63-70
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• 2012

In this paper an extension to an original active shape model(ASM) for facial feature extraction is presented. The original ASM suffers from poor shape alignment by aligning the shape model to a new instant of the object in a given image using a simple similarity transformation. It exploits only informations such as scale, rotation and shift in horizontal and vertical directions, which does not cope effectively with the complex pose variation. To solve the problem, new shape alignment with 6 degrees of freedom is derived, which corresponds to an affine transformation. Another extension is to speed up the calculation of the Mahalanobis distance for 2-D profiles by trimming the profile covariance matrices. Extensive experiment is conducted with several images of varying poses to check the performance of the proposed method to segment the human faces.