• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2001.12a
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• pp.299-302
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• 2001

The statistical analysis of the feature extraction and the neural networks are proposed to recognize a human face. In the preprocessing step, the normalized skin color map with Gaussian functions is employed to extract the region of face candidate. The feature information in the region of the face candidate is used to detect the face region. In the recognition step, as a tested, the 120 images of 10 persons are trained by the backpropagation algorithm. The images of each person are obtained from the various direction, pose, and facial expression. Input variables of the neural networks are the geometrical feature information and the feature information that comes from the eigenface spaces. The simulation results of$.$10 persons show that the proposed method yields high recognition rates.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2001.05a
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• pp.656-659
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• 2001

Human have a notable ability to recognize faces, which is one of the most common visual feature in our environment. In regarding face pattern, just like other natural object, a geometrical interpretation of face is difficult to achieve. In this paper, we present wavelet based approach to extract the face features. Proposed approach is similar to the feature based scheme, where the feature is derived from the intensity data without detecting any knowledge of the significant feature. Topological graphs are involved to represent some relations between facial features. In our experiments, proposed approach is less sensitive to the intensity variation.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.9
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• pp.822-826
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• 2001

The statistical analysis of the feature extraction and the neural networks are proposed to recognize a human face. In the preprocessing step, the normalized skin color map with Gaussian functions is employed to extract the region of face candidate. The feature information in the region of the face candidate is used to detect the face region. In the recognition step, as a tested, the 120 images of 10 persons are trained by the backpropagation algorithm. The images of each person are obtained from the various direction, pose, and facial expression. Input variables of the neural networks are the geometrical feature information and the feature information that comes from the eigenface spaces. The simulation results of 10 persons show that the proposed method yields high recognition rates.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.5 no.2
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• pp.349-355
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• 2001

Human have a notable ability to recognize faces, which is one of the most common visual feature in our environment. In regarding face pattern, just like other natural object, a geometrical interpretation of face is difficult to achieve. In this paper, we present wavelet based approach to extract the face features. Proposed approach is similar to the feature based scheme, where the feature is derived from the intensity data without detecting any knowledge of the significant feature. Topological graphs are involved to represent some relations between facial features. In our experiments, proposed approach is less sensitive to the intensity variation.

• The KIPS Transactions:PartB
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• v.11B no.3
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• pp.257-266
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• 2004

This paper proposes caricature generation system that uses shading mechanism that extracts textural features of face. Using this method, we can get more realistic caricature. Since this system If vector-based, the generated character's face has no size limit and constraint. so it is available to transform the shape freely and to apply various facial expressions to 2D face. Moreover, owing to the vector file's advantage, It can be used in mobile environment as small file size This paper presents methods that generate vector-based face, create shade and synthesize the shade with the vector face.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.7
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• pp.2496-2511
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• 2014

Face recognition is considered a complex biometrics in the field of image processing mainly due to the constraints imposed by variation in the appearance of facial images. These variations in appearance are affected by differences in expressions and/or occlusions (sunglasses, scarf etc.). This paper discusses incremental Kernel Fisher Discriminate Analysis on sub-classes for dealing with partial occlusions and variant expressions. This framework focuses on the division of classes into fixed size sub-classes for effective feature extraction. For this purpose, it modifies the traditional Linear Discriminant Analysis into incremental approach in the kernel space. Experiments are performed on AR, ORL, Yale B and MIT-CBCL face databases. The results show a significant improvement in face recognition.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.7
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• pp.119-128
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• 1994

In this paper, we propose a method for facial feature extraction and recognition algorithm using neural networks. First we extract a face part from the background image based on the knowledge that it is located in the center of an input image and that the background is homogeneous. Then using vertical and horizontal projections. We extract features from the separated face image using knowledge base of human faces. In the recognition step we use the back propagation algorithm of the neural networks and in the learning step to reduce the computation time we vary learning and momentum rates. Our technique recognizes 6 women and 14 men correctly.

• Journal of the Korea Society of Computer and Information
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• v.20 no.8
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• pp.15-21
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• 2015

This study proposed a Sasang constitution classification system that can increase the objectivity and reliability of Sasang constitution diagnosis using the image of frontal face, in order to solve problems in the subjective classification of Sasang constitution based on Sasang constitution specialists' experiences. For classification, characteristics indicating the shapes of the eyes, nose, mouth and chin were defined, and such characteristics were extracted using the morphological statistic analysis of face images. Then, Sasang constitution was classified through a SVM (Support Vector Machine) classifier using the extracted characteristics as its input, and according to the results of experiment, the proposed system showed a correct recognition rate of 93.33%. Different from existing systems that designate characteristic points directly, this system showed a high correct recognition rate and therefore it is expected to be useful as a more objective Sasang constitution classification system.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.3
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• pp.1390-1403
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• 2016

Facial expression recognition (FER) plays a very significant role in computer vision, pattern recognition, and image processing applications such as human computer interaction as it provides sufficient information about emotions of people. For video-based facial expression recognition, depth cameras can be better candidates over RGB cameras as a person's face cannot be easily recognized from distance-based depth videos hence depth cameras also resolve some privacy issues that can arise using RGB faces. A good FER system is very much reliant on the extraction of robust features as well as recognition engine. In this work, an efficient novel approach is proposed to recognize some facial expressions from time-sequential depth videos. First of all, efficient Local Binary Pattern (LBP) features are obtained from the time-sequential depth faces that are further classified by Generalized Discriminant Analysis (GDA) to make the features more robust and finally, the LBP-GDA features are fed into Hidden Markov Models (HMMs) to train and recognize different facial expressions successfully. The depth information-based proposed facial expression recognition approach is compared to the conventional approaches such as Principal Component Analysis (PCA), Independent Component Analysis (ICA), and Linear Discriminant Analysis (LDA) where the proposed one outperforms others by obtaining better recognition rates.

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.3
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• pp.143-150
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• 2016

In this paper, we propose Korean monophthong recognition method optimizing muscle mixing based on facial surface EMG signals. We observed that EMG signal patterns and muscle activity may vary according to Korean monophthong pronunciation. We use RMS, VAR, MMAV1, MMAV2 which were shown high recognition accuracy in previous study and Cepstral Coefficients as feature extraction algorithm. And we classify Korean monophthong by QDA(Quadratic Discriminant Analysis) and HMM(Hidden Markov Model). Muscle mixing optimized using input data in training phase, optimized result is applied in recognition phase. Then New data are input, finally Korean monophthong are recognized. Experimental results show that the average recognition accuracy is 85.7% in QDA, 75.1% in HMM.