• The Journal of Korean Institute of Communications and Information Sciences
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• v.39A no.2
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• pp.92-99
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• 2014

This paper proposes the method of facial expression recognition based on decision tree structure. In the image of facial expression, ASM(Active Shape Model) and LBP(Local Binary Pattern) make the local features of a facial expressions extracted. The discriminant features gotten from local features make the two facial expressions of all combination classified. Through the sum of true related to classification, the combination of facial expression and local region are decided. The integration of branch classifications generates decision tree. The facial expression recognition based on decision tree shows better recognition performance than the method which doesn't use that.

• Journal of the Korea Convergence Society
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• v.9 no.4
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• pp.47-55
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• 2018

In this paper, we propose a novel face recognition(FR) method that takes advantage of combining weighted deep local features extracted from multiple Deep Convolutional Neural Networks(DCNNs) learned with a set of facial local regions. In the proposed method, the so-called weighed deep local features are generated from multiple DCNNs each trained with a particular face local region and the corresponding weight represents the importance of local region in terms of improving FR performance. Our weighted deep local features are applied to Joint Bayesian metric learning in conjunction with Nearest Neighbor(NN) Classifier for the purpose of FR. Systematic and comparative experiments show that our proposed method is robust to variations in pose, illumination, and expression. Also, experimental results demonstrate that our method is feasible for improving face recognition performance.

• Journal of KIISE
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• v.44 no.7
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• pp.692-702
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• 2017

Face detection using the LBP based feature descriptor has issues in that it can not represent spatial information between facial shape and facial components such as eyes, nose and mouth. To address these issues, in previous research, a facial image was divided into a number of square sub-regions. However, since the sub-regions are divided into different numbers and sizes, the division criteria of the sub-region suitable for the database used in the experiment is ambiguous, the dimension of the LBP histogram increases in proportion to the number of sub-regions and as the number of sub-regions increases, the sensitivity to facial orientation rotation increases significantly. In this paper, we present a novel facial region segmentation method that can solve in-plane rotation issues associated with LBP based feature descriptors and the number of dimensions of feature descriptors. As a result, the proposed method showed detection accuracy of 99.0278% from a single facial image rotated in orientation.

• Journal of Information Processing Systems
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• v.9 no.1
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• pp.173-188
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• 2013

In Facial Expression Recognition Systems (FERS), only particular regions of the face are utilized for discrimination. The areas of the eyes, eyebrows, nose, and mouth are the most important features in any FERS. Applying facial features descriptors such as the local binary pattern (LBP) on such areas results in an effective and efficient FERS. In this paper, we propose an automatic facial expression recognition system. Unlike other systems, it detects and extracts the informative and discriminant regions of the face (i.e., eyes, nose, and mouth areas) using Haar-feature based cascade classifiers and these region-based features are stored into separate image files as a preprocessing step. Then, LBP is applied to these image files for facial texture representation and a feature-vector per subject is obtained by concatenating the resulting LBP histograms of the decomposed region-based features. The one-vs.-rest SVM, which is a popular multi-classification method, is employed with the Radial Basis Function (RBF) for facial expression classification. Experimental results show that this approach yields good performance for both frontal and near-frontal facial images in terms of accuracy and time complexity. Cohn-Kanade and JAFFE, which are benchmark facial expression datasets, are used to evaluate this approach.

• Journal of Korea Multimedia Society
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• v.20 no.5
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• pp.748-757
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• 2017

In order to recognize the facial expressions, good features that can express the facial expressions are essential. It is also essential to find the characteristic areas where facial expressions appear discriminatively. In this study, we propose a directional LBP feature for facial expression recognition and a method of finding directional LBP operation and feature region for facial expression classification. The proposed directional LBP features to characterize facial fine micro-patterns are defined by LBP operation factors (direction and size of operation mask) and feature regions through AdaBoost learning. The facial expression classifier is implemented as a SVM classifier based on learned discriminant region and directional LBP operation factors. In order to verify the validity of the proposed method, facial expression recognition performance was measured in terms of accuracy, sensitivity, and specificity. Experimental results show that the proposed directional LBP and its learning method are useful for facial expression recognition.

• Archives of Craniofacial Surgery
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• v.13 no.2
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• pp.139-142
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• 2012

Purpose: To reconstruct the midface, local flaps such as nasolabial flaps have been frequently used. These local flaps, however, have the shortcomings of requiring a secondary operation or limitations in the movement of the flap. Thus, new methods have been developed. This paper reports a case wherein the basal cell carcinoma on the cheek was resected and the skin and soft tissue defect was successfully treated using a facial artery perforator flap. Methods: A 68-year-old female consulted the authors on the basal cell carcinoma that developed on her cheek. The mass was fully resected and revealed a $2.3{\times}2.3cm$ defective region. Using a Doppler ultrasonography, the facial artery path was traced, and using a loupe magnification, the facial artery perforator flap was elevated and the defective region was covered with the flap. Results: The flap developed early venous congestion, but it disappeared without any treatment. Six months after the surgery, the patient was satisfied with the postoperative result. Conclusion: The facial artery perforator flap has a thin pedicle. It offers a big arc of the rotation that allows free movement and one-stage operation. These strengths make the method useful for the reconstruction of the midface among other procedures.

• Archives of Craniofacial Surgery
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• v.22 no.3
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• pp.135-140
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• 2021

Background: Wide surgical excision is the gold standard for basal cell carcinoma (BCC) treatment. Typically, resection requires a safety margin ≥ 4 mm. We aimed to confirm BCC excisions' cancer recurrence rate and safety on the facial region with new safety margins. Methods: We included patients with primary BCC on the facial region who underwent wide excision with 2- or 3-mm safety margins at our institution between January 2010 and December 2018. Medical records were reviewed to confirm the epidemiology and surgical information. Recurrence was confirmed by physical examination through regular 6-month follow-up. Results: We included 184 out of 233 patients in this study after applying the exclusion criteria. The mean age and follow-up period were 71.2±10.2 years and 29.3±13.5 months, respectively. The predominantly affected area was the nose (95 cases); a V-Y advancement flap was the most commonly used surgical method. There were two cases of recurrence in the 2 mm margin group and one recurrence in the group resected with 3 mm margins. Conclusion: In this large cohort study, we found 2-3 mm excision margins can yield enough safety in facial BCCs. The recurrence rates were found to be comparable with those reported after wider margins.

• Journal of The Korean Dental Society of Anesthesiology
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• v.4 no.1 s.6
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• pp.21-24
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• 2004

Facial nerve paralysis following the administration of a local anaesthetic can be alarming. By reading reports of such incidents, dentists who find themselves in similar situations will be able to reassure their patients and act accordingly. This article reviews the classifications of anesthetic complication, local complications, etiology, prevention, treatment of facial nerve paralysis fellowing the administration of a local anaesthetic. A thorough knowledge of the relevant anatomy pertinent to the various injections used in dental surgery is essential.

• Journal of KIISE:Software and Applications
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• v.34 no.5
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• pp.476-486
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• 2007

This paper proposes a resolution enhancement method which can reconstruct high-resolution facial images from single-frame, low-resolution facial images. The proposed method is derived from example-based reconstruction methods and the morphable face model. In order to improve the performance of the example-based reconstruction, we propose the region-based reconstruction method which can maintain the characteristics of local facial regions. Also, in order to use the capability of the morphable face model to face resolution enhancement problems, we define the extended morphable face model in which an extended face is composed of a low-resolution face, its interpolated high-resolution face, and the high-resolution equivalent, and then an extended face is separated by an extended shape vector and an extended texture vector. The encouraging results show that the proposed methods can be used to improve the performance of face recognition systems, particularly to enhance the resolution of facial images captured from visual surveillance systems.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.6
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• pp.89-101
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• 2014

In this paper, we proposed a new local micro pattern, Signed Local Directional Pattern(SLDP). SLDP uses information of edges to represent the face's texture. This can produce a more discriminating and efficient code than other state-of-the-art methods. Each micro pattern of SLDP is encoded by sign and its major directions in which maximum edge responses exist-which allows it to distinguish among similar edge patterns that have different intensity transitions. In this paper, we divide the face image into several regions, each of which is used to calculate the distributions of the SLDP codes. Each distribution represents features of the region and these features are concatenated into a feature vector. We carried out facial expression recognition with feature vectors and SVM(Support Vector Machine) on Cohn-Kanade and JAFFE databases. SLDP shows better classification accuracy than other existing methods.