• 한국HCI학회:학술대회논문집
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• 2009.02a
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• pp.449-454
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• 2009

The human face has "expression" as an important visual factor in contrast with general objects. For this reason, cartoonists draw shadow that emphasizes facial shape and facial expression in order to convey atmosphere of scene and trait of character. This shadow should be considered when doing cartoon rendering for facial expression although it is not an physical shading. This paper proposes a cartoon rendering system for facial expression based on shading techniques of real cartoonist. First of all, we searched such techniques of cartoonist through variety of collected cartoon images and defined shadow templates according to character's facial expression to do cartoon rendering diffently. After that, we demonstrated cartoon rendering system of facial expression on the basis of survey result that effectively emphasizes facial shape and facial expression. Finally, we showed the usefulness through the user questionnaire.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.01a
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• pp.689-694
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• 2009

We developed a facial image generating technique that can manipulate facial impressions. The present study applied this impression transferring method to higher-order impressions such as "elegance" or "attractiveness" and confirmed the psychological validity of this method using the semantic differential method. Subsequently, we applied this method to two types of cognitive experiments. First, we examined the contributions of texture and shape on the facial impressions by using those face images for which the impressions have already been quantitatively manipulated based on this method. Second, we used such stimuli to examine the effect of facial impressions and attractiveness on the "mere exposure effect." Thus, we concluded that the impression transfer vector method is an effective tool to quantitatively manipulate the facial impressions in various cognitive studies.

• Journal of Trauma and Injury
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• v.19 no.1
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• pp.28-34
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• 2006

Purpose: In maxillofacial surgery, proper preoperative diagnosis is very important in achieving good postoperative results. Although conventional CT scans are useful for visual representations of fractures, they cannot provide direct guidance for reconstructing facial bone fractures. However, the recent technology of multislice scanning has brought many clinical benefits to CT images. Direct correlations can be made between preoperative imaging data and operative planning. The aim of the current study is to evaluate the differences between conventional CT and multidetective three-dimensional CT(3D MDCT) measurements in craniofacial deformities. Methods: From January 2005 to November 2005, MDCT scans of 41 patients were evaluated by comparing them with conventional CT scans. The 3D MDCT images were assessed and reviewed by using a simple scoring system. Results: The 3D MDCT scans offered easy interpretation, facilitated surgical planning, and clarified postoperative results in malar complex fractures, mandibular fractures, and extensive maxillofacial fractures and cranioplasty. However, 3D MDCT images were not superior to conventional CT scans in the diagnosis of blowout fractures. Conclusion: In spite of its limitations, the 3D MDCT provided additional and more comprehensive information than the conventional CT for preoperative assessment of craniofacial deformities. Therefore, the 3D MDCT can be a useful tool for diagnosis and systematic treatment planning in craniofacial skeletal deformities.

• The Journal of the Korea Contents Association
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• v.4 no.3
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• pp.107-113
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• 2004

This paper improved the performance of the face recognition system using the PCA/LDA hybrid method based on the facial geometric feature and the Wavelet transform. Because the previous PCA/LDA methods have measured the similarity according to the formal dispersion, they could not reflect facial boundaries exactly In order to recover this defect, this paper proposed the method using the distance between eyes and mouth. If the difference of the measured distances on the query and the training images is over the given threshold, then the method reorders the candidate images according to energy feature vectors of eyes, a nose, and a chin. To evaluate the performance of the proposed method the computer simulations have been performed with four hundred facial images in the ORL database. The results showed that our method improves about 4％ recognition rate over the previous PCA/LDA method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.4
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• pp.2129-2147
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• 2019

This paper introduces an adaptive face recognition method based on a Novel Incremental Kernel Nonparametric Discriminant Analysis (IKNDA) that is able to learn through time. More precisely, the IKNDA has the advantage of incrementally reducing data dimension, in a discriminative manner, as new samples are added asynchronously. Thus, it handles dynamic and large data in a better way. In order to perform face recognition effectively, we combine the Gabor features and the ordinal measures to extract the facial features that are coded across local parts, as visual primitives. The variegated ordinal measures are extraught from Gabor filtering responses. Then, the histogram of these primitives, across a variety of facial zones, is intermingled to procure a feature vector. This latter's dimension is slimmed down using PCA. Finally, the latter is treated as a facial vector input for the advanced IKNDA. A comparative evaluation of the IKNDA is performed for face recognition, besides, for other classification endeavors, in a decontextualized evaluation schemes. In such a scheme, we compare the IKNDA model to some relevant state-of-the-art incremental and batch discriminant models. Experimental results show that the IKNDA outperforms these discriminant models and is better tool to improve face recognition performance.

• Imaging Science in Dentistry
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• v.49 no.1
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• pp.35-43
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• 2019

Purpose: The goal of this study was to assess the accuracy and reliability of a low-cost portable scanner (Scanify) for imaging facial casts compared to a previously validated portable digital stereophotogrammetry device (Vectra H1). This in vitro study was performed using 2 facial casts obtained by recording impressions of the authors, at King's College London Academic Centre of Reconstructive Science. Materials and Methods: The casts were marked with anthropometric landmarks, then digitised using Scanify and Vectra H1. Computed tomography (CT) scans of the same casts were performed to verify the validation of Vectra H1. The 3-dimensional (3D) images acquired with each device were compared using linear measurements and 3D surface analysis software. Results: Overall, 91% of the linear Scanify measurements were within 1 mm of the corresponding reference values. The mean overall surface difference between the Scanify and Vectra images was <0.3mm. Significant differences were detected in depth measurements. Merging multiple Scanify images produced significantly greater registration error. Conclusion: Scanify is a very low-cost device that could have clinical applications for facial imaging if imaging errors could be corrected by a future software update or hardware revision.

• Journal of Information Processing Systems
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• v.17 no.3
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• pp.556-570
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• 2021

The existing video expression recognition methods mainly focus on the spatial feature extraction of video expression images, but tend to ignore the dynamic features of video sequences. To solve this problem, a multi-mode convolution neural network method is proposed to effectively improve the performance of facial expression recognition in video. Firstly, OpenFace 2.0 is used to detect face images in video, and two deep convolution neural networks are used to extract spatiotemporal expression features. Furthermore, spatial convolution neural network is used to extract the spatial information features of each static expression image, and the dynamic information feature is extracted from the optical flow information of multiple expression images based on temporal convolution neural network. Then, the spatiotemporal features learned by the two deep convolution neural networks are fused by multiplication. Finally, the fused features are input into support vector machine to realize the facial expression classification. Experimental results show that the recognition accuracy of the proposed method can reach 64.57% and 60.89%, respectively on RML and Baum-ls datasets. It is better than that of other contrast methods.

• Journal of Korean Academy of Oral and Maxillofacial Radiology
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• v.22 no.2
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• pp.283-290
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• 1992

The purpose of this study was to evaluate the spatial relationship of facial bone more accurately. For this study, the three-dimensional images of dry skull were reconstructed using computer image analysis system and three-dimensional reconstructive program involved CT. The obtained results were as follows: 1. Three-dimensional reconstructive CT results in images that have better resolution and more contrast 2. It showed good marginal images of anatomical structure on both three-dimensional CT and computer image analysis system, but the roof of orbit, the lacrimal bone and the squamous portion of temporal bone were hardly detectable. 3. The partial loss of image data were observed during the regeneration of saved image data on three-dimensional CT. 4. It saved the more time for reconstruction of three-dimensional images using computer image analysis system. But, the capacity of hardware was limited for inputting of image data and three-dimensional reconstructive process. 5. We could observe the spatial relationship between the region of interest and the surrounding structures by three-dimensional reconstructive images without invasive method.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.35C no.10
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• pp.48-58
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• 1998

We present a method that can recognize face images using independent component analysis that is used mainly for blind sources separation in signal processing. We assumed that a face image can be expressed as the sum of a set of statistically independent feature images, which was obtained by using independent component analysis. Face recognition was peformed by projecting the input image to the feature image space and then by comparing its projection components with those of stored reference images. We carried out face recognition experiments with a database that consists of various varied face images (total 400 varied facial images collected from 10 per person) and compared the performance of our method with that of the eigenface method based on principal component analysis. The presented method gave better results of recognition rate than the eigenface method did, and showed robustness to the random noise added in the input facial images.

• Journal of the Korea Society of Computer and Information
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• v.11 no.3
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• pp.151-159
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• 2006

In this paper. we Present an estimation method of a face pose by using two camera images. First, it finds corresponding facial feature points of eyebrow, eye and lip from two images After that, it computes three dimensional location of the facial feature points by using the triangulation method of stereo vision techniques. Next. it makes a triangle by using the extracted facial feature points and computes the surface normal vector of the triangle. The surface normal of the triangle represents the direction of the face. We applied the computed face pose to display a 3D face model. The experimental results show that the proposed method extracts correct face pose.