• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2002.11a
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• pp.240-243
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• 2002

In this paper, we propose a face region detection based on skin-color distribution and facial feature extraction algorithm in color still images. To extract face region, we transform color using general skin-color distribution. Facial features are extracted by edge transformation. This detection process reduces calculation time by a scale-down scanning from segmented region. we can detect face region in various facial Expression, skin-color deference and tilted face images.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.11
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• pp.193-201
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• 2014

This paper suggests a facial expression recognition system using face detection, face alignment, facial unit extraction, and training and testing algorithms based on AdaBoost classifiers. First, we find face region by a face detector. From the results, face alignment algorithm extracts feature points. The facial units are from a subset of action units generated by combining the obtained feature points. The facial units are generally more effective for smaller-sized databases, and are able to represent the facial expressions more efficiently and reduce the computation time, and hence can be applied to real-time scenarios. Experimental results in real scenarios showed that the proposed system has an excellent performance over 90% recognition rates.

• Journal of Korea Multimedia Society
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• v.21 no.8
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• pp.917-924
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• 2018

Recently, researches using LBP and SVM have been performed as one of the image - based methods for facial emotion recognition. LBP, introduced by Ojala et al., is widely used in the field of image recognition due to its high discrimination of objects, robustness to illumination change, and simple operation. In addition, CS(Center-Symmetric)-LBP was used as a modified form of LBP, which is widely used for face recognition. In this paper, we propose a method to detect four facial expressions such as expressionless, happiness, surprise, and anger using deep neural network. The validity of the proposed method is verified using accuracy. Based on the existing LBP feature parameters, it was confirmed that the method using the deep neural network is superior to the method using the Adaboost and SVM classifier.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.13 no.3
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• pp.206-211
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• 2020

It is Face recognition is a very important process in image monitoring and it is a form of biometric technology. The recognition process involves many variables and is highly complex, so the software development has only begun recently with the development of hardware. Face detection technology using the CCTV is a process that precedes face analysis, and it is a technique that detects where the face is in the image. Research in face detection and recognition has been difficult because the human face reacts sensitively to different environmental conditions, such as lighting, color of skin, direction, angle and facial expression. The utility and importance of face recognition technology is coming into the limelight over time, but many aspects are being overlooked in the facial area detection technology that must precede face recognition. The system in this paper can detect tilted faces that cannot be detected by the AdaBoost detector and It could also be used to detect other objects.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.101.2-101
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• 2001

This paper proposes a novel face recognition algorithm to recognize human face robustly under various conditions, such as changes of pose, illumination, and expression, etc. at indoor environments. A conventional automatic face recognition system consists of the detection and the recognition part. Generally, the detection part is dominant over the other part in the estimating whole recognition rate. So, in this paper, we suggest the view-specific eigenface method as preprocessor to estimate various poses of the face in the input image. Then, we apply the Enhanced FLD Models (EFM) to the result of it, twice. Because, the EFM recognizes human face, and reduces the error of standardization effectively. To deal with view-varying problem, we build one basis vector set for each view individually. Finally, the dimensionalities of ...

• Proceedings of the Korea Information Processing Society Conference
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• 2011.11a
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• pp.351-354
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• 2011

This paper discusses on facial features extraction based on proposed skin color model. Different parts of face from input image are segmented based on skin color model. Moreover, this paper also discusses on concept to detect the eye and mouth position on face. A height and width ratio (${\delta}=1.1618$) based technique is also proposed to accurate detection of face region from the segmented image. Finally, we have cropped the desired part of the face. This exactly exacted face part is useful for face recognition and detection, facial feature analysis and expression analysis. Experimental results of propose method shows that the proposed method is robust and accurate.

• Journal of Internet Computing and Services
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• v.5 no.6
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• pp.79-91
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• 2004

Various facial pose detection and recognition has been a difficult problem. The problem is due to the fact that the distribution of various poses in a feature space is mere dispersed and more complicated than that of frontal faces, This thesis proposes a robust pose-expression-invariant face recognition method in order to overcome insufficiency of the existing face recognition system. First, we apply the TSL color model for detecting facial region and estimate the direction of face using facial features. The estimated pose vector is decomposed into X-V-Z axes, Second, the input face is mapped by deformable template using this vectors and 3D CANDIDE face model. Final. the mapped face is transformed to frontal face which appropriates for face recognition by the estimated pose vector. Through the experiments, we come to validate the application of face detection model and the method for estimating facial poses, Moreover, the tests show that recognition rate is greatly boosted through the normalization of the poses and expressions.

• 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.

• 한국HCI학회:학술대회논문집
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• 2008.02c
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• pp.172-177
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• 2008

Biometrics is essential for person identification because of its uniqueness from each individuals. Face recognition technology has advantage over other biometrics because of its convenience and non-intrusive characteristics. In this paper, we will present a overview of face recognition technology including face detection, feature extraction, and face recognition system. For face detection, we will describe template based method and face component based approach. PCA and LDA approach will be discussed for feature extraction, and nearest neighbor classifiers -will be covered for matching. Large database and the standardized performance evaluation methodology is essential in order to support state-of-the-art face recognition system. Also, 3D based face recognition technology is the key solution for the pose, lighting and expression variations in many applications.

• Proceedings of the KIEE Conference
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• 2005.05a
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• pp.113-115
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• 2005

This thesis presents a method for 3D modeling of facial expression from frontal and mirror-reflected multi-image. Since the proposed system uses only one camera, two mirrors, and simple mirror's property, it is robust, accurate and inexpensive. In addition, we can avoid the problem of synchronization between data among different cameras. Mirrors located near one's cheeks can reflect the side views of markers on one's face. To optimize our system, we must select feature points of face intimately associated with human's emotions. Therefore we refer to the FDP (Facial Definition Parameters) and FAP (Facial Animation Parameters) defined by MPEG-4 SNHC (Synlhetic/Natural Hybrid Coding). We put colorful dot markers on selected feature points of face to detect movement of facial deformation when subject makes variety expressions. Before computing the 3D coordinates of extracted facial feature points, we properly grouped these points according to relative part. This makes our matching process automatically. We experiment on about twenty koreans the subject of our experiment in their late twenties and early thirties. Finally, we verify the performance of the proposed method tv simulating an animation of 3D facial expression.