• Archives of Plastic Surgery
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• v.33 no.4
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• pp.525-529
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• 2006

Purpose: Facial nerve injury is a rare but feared complication of face lift. Uncertainty as to the depth and vulnerability of the facial nerve in face lift dissection causes some surgeon, particularly novices, to dissect slowly. Excessively thin flaps can be made because of fear of nerve injury, contributing to skin slough. Methods: From September 1998 to February 2003, the authors operated on 34 aging face patients using classical face-lift. We had analysed about quantity of skin removal and degree of elevated flap. The authors have found quantity of skin removal was 1.5-2.0 cm, degree of elevated flap was 40-45 degree on average. Results: The authors performed preauricular pre-excision face-lift technique on 12 aging face patients from July 2003 to Feburary 2005 based on experienced surgery. This technique reduced fear of dissecting skin flap necrosis and facial nerve injury because of firmly attached pre-auricular skin removed in advance. Conclusions: We easily dissected SMAS without visual field disturbance, nerve damage and reduced operation time and bleeding loss compared to classical face-lift.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.11
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• pp.5605-5623
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• 2016

We propose a face recognition method that utilizes the LCP face descriptor. The proposed method applies a LoG mask to extract a face contour response, and employs the LCP algorithm to produce a binary pattern representation that ensures high recognition performance even under the changes in illumination, noise, and aging. The proposed LCP algorithm produces excellent noise reduction and efficiency in removing unnecessary information from the face by extracting a face contour response using the LoG mask, whose behavior is similar to the human eye. Majority of reported algorithms search for face contour response information. On the other hand, our proposed LCP algorithm produces results expressing major facial information by applying the threshold to the search area with only 8 bits. However, the LCP algorithm produces results that express major facial information with only 8-bits by applying a threshold value to the search area. Therefore, compared to previous approaches, the LCP algorithm maintains a consistent accuracy under varying circumstances, and produces a high face recognition rate with a relatively small feature vector. The test results indicate that the LCP algorithm produces a higher facial recognition rate than the rate of human visual's recognition capability, and outperforms the existing methods.

• Journal of Institute of Control, Robotics and Systems
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• v.15 no.1
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• pp.94-98
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• 2009

In this paper, we deal with a face recognition method for the emotional face images. Since the face recognition is one of the most natural and straightforward biometric methods, there have been various research works. However, most of them are focused on the expressionless face images and have had a very difficult problem if we consider the facial expression. In real situations, however, it is required to consider the emotional face images. Here, three basic human emotions such as happiness, sadness, and anger are investigated for the face recognition. And, this situation requires a robust face recognition algorithm then we use a fuzzy Fisher's Linear Discriminant (FLD) algorithm with the wavelet transform. The fuzzy Fisherface is a statistical method that maximizes the ratio of between-scatter matrix and within-scatter matrix and also handles the fuzzy class information. The experimental results obtained for the CBNU face databases reveal that the approach presented in this paper yields better recognition performance in comparison with the results obtained by other recognition methods.

• Proceedings of the IEEK Conference
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• 2002.06d
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• pp.69-72
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• 2002

Human face detection has many applications such as face recognition, face or facial feature tracking, pose estimation, and expression recognition. We present a new method for automatically segmentation and face detection in color images. Skin color alone is usually not sufficient to detect face, so we combine the color segmentation and shape analysis. The algorithm consists of two stages. First, skin color regions are segmented based on the chrominance component of the input image. Then regions with elliptical shape are selected as face hypotheses. They are certificated to searching for the facial features in their interior, Experimental results demonstrate successful detection over a wide variety of facial variations in scale, rotation, pose, lighting conditions.

• Journal of information and communication convergence engineering
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• v.5 no.1
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• pp.50-53
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• 2007

This paper presents face features detection and a new physiological neuro-fuzzy learning method by using two-dimensional variances based on variation of gray level and by learning for a statistical distribution of the detected face features. This paper reports a method to learn by not using partial face image but using global face image. Face detection process of this method is performed by describing differences of variance change between edge region and stationary region by gray-scale variation of global face having featured regions including nose, mouse, and couple of eyes. To process the learning stage, we use the input layer obtained by statistical distribution of the featured regions for performing the new physiological neuro-fuzzy algorithm.

• Proceedings of the Korea Information Processing Society Conference
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• 2019.10a
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• pp.1002-1003
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• 2019

Face recognition is an extensively researched area of computer vision. Visible, infrared, thermal, and 3D modalities have been used against various challenges of face recognition such as illumination, pose, expression, partial information, and disguise. In this paper we present a multi-modal approach to face recognition using convolutional neural networks. We use visible and thermal face images as two separate inputs to a multi-input deep learning network for face recognition. The experiments are performed on IRIS visible and thermal face database and high face verification rates are achieved.

• Journal of Korea Society of Industrial Information Systems
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• v.23 no.5
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• pp.9-17
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• 2018

Recently, the increasing interest of biometric systems has led to the creation of many researches of biometrics forgery. In order to solve this forgery problem, this paper proposes a method of determining whether a synthesized face made of artificaial intelligence is real face or fake face. The proposed algorithm consists of two steps. Firstly, we create the fake face images using various GAN (Generative Adversarial Networks) algorithms. After that, deep learning algorithm can classify the real face image and the generated face image. The experimental results shows that the proposed algorithm can detect the fake face image which looks like the real face. Also, we obtained the classification accuracy of 88.7%.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.05a
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• pp.359-362
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• 2016

Face is an information unique to each person such as Iris, fingerprints, etc,. Research on face recognition are in progress continuously from the past to the present. Through these research, various face recognition methods have appeared. Among these methods, there are face recognition algorithms using the face data composed in stereo. In this paper, Convolutional Neural Network with Stereo Images as input was used for face recognition. This method showed better performance than the result of stereo face recognition using PCA that is used frequently in face recognition.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.21 no.6
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• pp.153-160
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• 2011

Recently, the use of security surveillance system including CCTV is increasing due to the increase of terrors and crimes. At the same time, interest of face recognition at a distance using surveillance cameras has been increasing. Accordingly, we analyzed the performance of face recognition according to distance using PCA-based face recognition and interpolation. In this paper, we used Nearest, Bilinear, Bicubic, Lanczos3 interpolations to interpolate face image. As a result, we confirmed that existing interpolation have an few effect on performance of PCA-based face recognition and performance of PCA-based face recognition is improved by including face image according to distance in traning data.

• Journal of Korea Multimedia Society
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• v.15 no.4
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• pp.508-515
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• 2012

This paper presents a method that estimates face pose for a given face image and synthesizes any posed face images using Active Appearance Model(AAM). The AAM that having been successfully applied to various applications is an example-based learning model and learns the variations of training examples. However, with a single model, it is difficult to handle large pose variations of face images. This paper proposes to build a model covering only a small range of angle for each pose. Then, with a proper model for a given face image, we can achieve accurate pose estimation and synthesis. In case of the model used for pose estimation was not trained with the angle to synthesize, we solve this problem by training the linear relationship between the models in advance. In the experiments on Yale B public face database, we present the accurate pose estimation and pose synthesis results. For our face database having large pose variations, we demonstrate successful frontal pose synthesis results.