• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.1
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• pp.82-89
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• 2015

The researches of image recognition have been processed traditionally. Especially, face recognition technology has been received attractions with advance and applied to various areas according as camera sensor embedded into many devices such as smart phone. In this study, we design and develop a feature vector generation technique of face for making animation caricatures using methods for face detection which are previous stage of face recognition. At first, we detect both face region and detailed eye region of component element by Viola&Johns's realtime detection method which are called as ROI(Region Of Interest). And then, we generate feature vectors of eye region by utilizing factors as opposed to the periphery and by using appearance information of eye. At this point, we focus on the embedded information in many color spaces to overcome the problems which can be occurred by using one color space. We propose a feature vector generation method using information from many color spaces. Finally, we experiment the test of feature vector generation by the proposed method with enough quantity of sample picture data and evaluate the proposed method for factors of estimating performance such as error rate, accuracy and generation time.

• The KIPS Transactions:PartB
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• v.16B no.4
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• pp.299-308
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• 2009

AAM(Active Appearance Model) is an algorithm to extract face feature points with statistical models of shape and texture information based on PCA(Principal Component Analysis). This method is widely used for face recognition, face modeling and expression recognition. However, the detection performance of AAM algorithm is sensitive to initial value and the AAM method has the problem that detection error is increased when an input image is quite different from training data. Especially, the algorithm shows high accuracy in case of closed lips but the detection error is increased in case of opened lips and deformed lips according to the facial expression of user. To solve these problems, we propose the improved AAM algorithm using lip feature points which is extracted based on a new lip detection algorithm. In this paper, we select a searching region based on the face feature points which are detected by AAM algorithm. And lip corner points are extracted by using Canny edge detection and histogram projection method in the selected searching region. Then, lip region is accurately detected by combining color and edge information of lip in the searching region which is adjusted based on the position of the detected lip corners. Based on that, the accuracy and processing speed of lip detection are improved. Experimental results showed that the RMS(Root Mean Square) error of the proposed method was reduced as much as 4.21 pixels compared to that only using AAM algorithm.

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.7
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• pp.67-77
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• 2016

In this paper, we propose an algorithm for open/closed eye detection based on modified Hausdorff distance. The proposed algorithm consists of two parts, face detection and open/closed eye detection parts. To detect faces in an image, MCT (Modified Census Transform) is employed based on characteristics of the local structure which uses relative pixel values in the area with fixed size. Then, the coordinates of eyes are found and open/closed eyes are detected using MHD (Modified Hausdorff Distance) in the detected face region. Firstly, face detection process creates an MCT image in terms of various face images and extract criteria features by PCA(Principle Component Analysis) on offline. After extraction of criteria features, it detects a face region via the process which compares features newly extracted from the input face image and criteria features by using Euclidean distance. Afterward, the process finds out the coordinates of eyes and detects open/closed eye using template matching based on MHD in each eye region. In performance evaluation, the proposed algorithm achieved 94.04% accuracy in average for open/closed eye detection in terms of test video sequences of gray scale with 30FPS/$320{\times}180$ resolution.

• Proceedings of the Korean Information Science Society Conference
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• 2002.10d
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• pp.538-540
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• 2002

본 논문에서는 실시간 카메라 입력 영상에 적합한 얼굴 검출을 위해 다양한 외부적 환경에 덜 민감한 새로운 알고리즘을 제안한다. 빛이나 조명의 영향에 의한 오류를 방지하기 위해 전처리 과정을 포함시키고 형판 정합방법의 단점을 개선하기 위해 얼굴 인식에서 주로 쓰이는 방법인 주성분 분석(PCA :Principal Component Analyses) 변환을 적용하고. 생성된 주성분(Principal Component)을 선형 판별 분석(LDA: Linear Discriminant Analysis)의 입력으로 사용하는 방법을 통해 얼굴을 검출하도록 하였다. 실험을 위해 실제 환경과 같은 6개 카테고리의 동영상을 중심으로 실험한 결과, 본 논문에서 제안하는 방법이 기존의 PCA만을 이용한 방법보다 좋은 성능을 보여줌을 알 수 있었다.

• Journal of KIISE:Software and Applications
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• v.30 no.7_8
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• pp.773-779
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• 2003

Locating exact position of facial components is a key preprocessing for realizing highly accurate and reliable face recognition schemes. In this paper, we propose a simple but powerful method for detecting isolated facial components such as eyebrows, eyes, and a mouth, which are horizontally oriented and have relatively dark gray levels. The method is based on the shape-resolving locally optimum thresholding that may guarantee isolated detection of each component. We show that pure facial regions can be determined by grouping facial features satisfying simple geometric constraints on unique facial structure. In the test for over 1000 images in the AR -face database, pure facial regions were detected correctly for each face image without wearing glasses. Very few errors occurred in the face images wearing glasses with a thick frame because of the occluded eyebrow -pairs. The proposed scheme may be best suited for the later stage of classification using either the mappings or a template matching, because of its capability of handling rotational and translational variations.

• Journal of Digital Contents Society
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• v.11 no.4
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• pp.417-424
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• 2010

In this paper the real-time face region was detected by suggesting the rectangular feature-based classifier and the robust detection algorithm that satisfied the efficiency of computation and detection performance was suggested. By using the detected face region as a recognition input image, in this paper the face recognition method combined with PCA and the multi-layer network which is one of the intelligent classification was suggested and its performance was evaluated. As a pre-processing algorithm of input face image, this method computes the eigenface through PCA and expresses the training images with it as a fundamental vector. Each image takes the set of weights for the fundamental vector as a feature vector and it reduces the dimension of image at the same time, and then the face recognition is performed by inputting the multi-layer neural network.

• Proceedings of the IEEK Conference
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• 2002.06d
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• pp.57-60
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• 2002

Facial features are often used for human computer interface(HCI). This paper proposes a method to detect facial features using color and facial geometry information. Face region is first extracted by using color information, and then the pupils are detected by applying a separability filter and facial geometry constraints. Mouth is also extracted from Cr(coded red) component. Experimental results shows that the proposed detection method is robust to a wide range of facial variation in position, scale, color and gaze.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.41 no.4
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• pp.45-50
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• 2004

In this paper, we propose a new PCA-based LDA Mixture Algorithm(PLMA) for real-time face recognition system. This system greatly consists of the two parts: 1) face extraction part; 2) face recognition part. In the face extraction part we applied subtraction image, color filtering, eyes and mouth region detection, and normalization method, and in the face recognition part we used the method mixing PCA and LDA in extracted face candidate region images. The existing recognition system using only PCA showed low recognition rates, and it is hard in the recognition system using only LDA to apply LDA to the input images as it is when the number of image pixels ire small as compared with the training set. To overcome these shortcomings, we reduced dimension as we apply PCA to the normalized images, and apply LDA to the compressed images, therefore it is possible for us to do real-time recognition, and we are also capable of improving recognition rates. We have experimented using self-organized DAUface database to evaluate the performance of the proposed system. The experimental results show that the proposed method outperform PCA, LDA and ICA method within the framework of recognition accuracy.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.7 no.6
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• pp.1312-1317
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• 2003

This paper presents an integrated approach to unconstrained face recognition in arbitrary scenes. The front end of the system comprises of a scale and pose tolerant face detector. Scale normalization is achieved through novel combination of a skin color segmentation and log-polar mapping procedure. Principal component analysis is used with the multi-view approach proposed in[10] to handle the pose variations. For a given color input image, the detector encloses a face in a complex scene within a circular boundary and indicates the position of the nose. Next, for recognition, a radial grid mapping centered on the nose yields a feature vector within the circular boundary. As the width of the color segmented region provides an estimated size for the face, the extracted feature vector is scale normalized by the estimated size. The feature vector is input to a trained neural network classifier for face identification. The system was evaluated using a database of 20 person's faces with varying scale and pose obtained on different complex backgrounds. The performance of the face recognizer was also quite good except for sensitivity to small scale face images. The integrated system achieved average recognition rates of 87% to 92%.

• Journal of the Korea Computer Industry Society
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• v.4 no.10
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• pp.631-642
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• 2003

We propose an approach to extract and to classify facial features into some classes from one's photo as prepared classification standards to generate one's avatar. Facial Feature Extraction and Classification was executed at eyes, nose, lips, jaw separately and I presented each facial features and classification standards. Extracted Facial Features are used for calculation to features of professional designer's facial component images. Then, most similar facial component images are mapped onto avatar's vector face.