• The Journal of the Korea Contents Association
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• v.4 no.4
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• pp.163-169
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• 2004

In this paper. we propose a shaded recognition method using symmetric feature. When the existing PCA is applied to shaded face images, the recognition rate is decreased. To improve the recognition rate, we use facial symmetry. If the difference of light and shade is greater than a threshold value, we make a mirror image by replacing the dark side with the bright side symmetrically Then the mirror image is compared with a query image. We compare the performance of the proposed algorithm with the existing algorithms such as PCA, PCA without three eigenfaces and histogram equalization methods. The recognition rate of our method shows $98.889\%$ with the excellent result.

• Journal of Oral Medicine and Pain
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• v.36 no.4
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• pp.253-259
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• 2011

Numb chin syndrome, is a rare neuropathy, characterized by facial and oral numbness restricted to the distribution of the mental nerve. Even though this neuropathy is uncommon, but this still has an important clinical meaning because it can be related with a malignancy. Because orofacial symptoms can even present the first clinical feature of a malignancy, dentists should pay careful attention to their meaning and importance to detect the malignant tumor early. Moreover, patients who present with a sudden numbness on chin should be investigated for the undiagnosed malignancy. In this report, we described a patient with stabbing orofacial pain and numbness of chin who was diagnosed with diffuse large B-cell lymphoma and placed the importance on the diagnosis of NCS.

• Clinical and Experimental Pediatrics
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• v.59 no.sup1
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• pp.19-24
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• 2016

Constitutional interstitial deletions of the long arm of chromosome 5 (5q) are quite rare, and the corresponding phenotype is not yet clearly delineated. Severe mental retardation has been described in most patients who present 5q deletions. Specifically, the interstitial deletion of chromosome 5q33.3q35.1, an extremely rare chromosomal aberration, is characterized by mental retardation, developmental delay, and facial dysmorphism. Although the severity of mental retardation varies across cases, it is the most common feature described in patients who present the 5q33.3q35.1 deletion. Here, we report a case of a de novo deletion of 5q33.3q35.1, 46,XY,del(5)(q33.3q35.1) in an 11-year-old boy with mental retardation; to the best of our knowledge this is the first case in Korea to be reported. He was diagnosed with severe mental retardation, developmental delay, facial dysmorphisms, dental anomalies, and epilepsy. Chromosomal microarray analysis using the comparative genomic hybridization array method revealed a 16-Mb-long deletion of 5q33. 3q35.1(156,409,412-172,584,708)x1. Understanding this deletion may help draw a rough phenotypic map of 5q and correlate the phenotypes with specific chromosomal regions. The 5q33.3q35.1 deletion is a rare condition; however, accurate diagnosis of the associated mental retardation is important to ensure proper genetic counseling and to guide patients as part of long-term management.

• Journal of the Institute of Convergence Signal Processing
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• v.2 no.2
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• pp.1-6
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• 2001

Eigenface method in face recognition is useful due to its insensitivity to large variations in facial expression and facial details. However its low recognition rate necessitates additional researches. In this paper, we present an efficient method for improving the recognition rate in face recognition using eigenface feature. For this, we performs a comparative study of three different classifiers which are i) a single prototype (SP) classifier, ii) a nearest neighbor (NN) classifier, and iii) a standard feedforward neural network (FNN) classifier. By evaluating and analyzing the performance of these three classifiers, we shows that the distribution of eigenface features of face image is not compact and that selections of classifier and sample training data are important for obtaining higher recognition rate. Our experiments with the ORL face database show that 1-NN classifier outperforms the SP and FNN classifiers. We have achieved a recognition rate of 91.0% by selecting sample trainging data properly and using 1-NN classifier.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.41 no.11
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• pp.1035-1045
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• 2017

Classification of age and gender has been carried out through different approaches such as facial-based and audio-based classifications. One of the limitations of facial-based methods is the reduced recognition rate over large distances, while another is the prerequisite of the faces to be located in front of the camera. Similarly, in audio-based methods, the recognition rate is reduced in a noisy environment. In contrast, gait-based methods are only required that a target person is in the camera. In previous works, the view point of a camera is only available as a side view and gait data sets consist of a standard gait, which is different from an ordinary gait in a real environment. We propose a feature extraction method using skeleton models from an RGB-D sensor by considering characteristics of age and gender using ordinary gait. Experimental results show that the proposed method could efficiently classify age and gender within a target group of individuals in real-life environments.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.4
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• pp.400-405
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• 2005

This paper analyzes the performance of a face recognition algorithm using the eigenfaces method. In the absence of robust personal recognition schemes, a biometric recognition system has essentially researched to improve their shortcomings. A face recognition system in biometries is widely researched in the field of computer vision and pattern recognition, since it is possible to comprehend intuitively our faces. The proposed system projects facial images onto a feature space that effectively expresses the significant variations among known facial images. The significant features are known as 'eigenfaces', because they are the eigenvectors(principal components) of the set of faces. The projection operation characterizes an individual face by a weighted sum of the eigenface features, and to recognize a particular face it is necessary only to compare these weights to those of known individuals. In order to analyze the performance of the system, we develop a face recognition system by using Harvard database in Harvard Robotics Laboratory. We present the recognition rate according to variations on the lighting condition, numbers of the employed eigenfaces, and existence of a pre-processing step. Finally, we construct a rejection curve in order to investigate the practicability of the recognition method using the eigenfaces.

• The Korean Journal of Pain
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• v.20 no.2
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• pp.174-180
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• 2007

Background: The diagnosis of trigeminal neuralgia (TN) is based on only clinical criteria. The purpose of this study was to estimate the clinical manifestations of TN patients treated at our pain clinic. Methods: A total of 341 patients with TN from Jan. 2004 to Dec. 2006 was evaluated the intensity, site, and onset of pain, facial sensation, duration of pain attack, pain free interval, triggering factors, and effects of the previous treatments with TN specific questionnaire and interview at the first visit of our pain clinic. Results: About 80% of the patients were over 50 years of age and 256 (75%) patients were women. Average durations from first attack of their pain and from current pain attack were 7 years and 16 weeks, respectively. The two most frequently involved trigeminal nerve branches were maxillary (40%) and mandibular (39%) branches. Three quarters of the total patients experienced only paroxysmal pain that lasted less than one minute. About 90% of patients had pain free period at least one time. Most common triggering factors were chewing (88%), brushing teeth (82%), washing face (79%), and talking (70%). Only 16 patients (5%) had no previous treatment and the others had more than one treatment, such as medication (68%) and interventional procedures (35%). The most common reasons for early discontinuation of carbamazepine were dizziness, ataxia, and vomiting. Conclusions: TN has specific clinical features of pain, which should be considered at diagnosis.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.5
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• pp.573-580
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• 2005

In this paper, we propose an emotion recognition using static and dynamic facial images to effectively design human interface. The proposed method is constructed by HMM(Hidden Markov Model), PCA(Principal Component) and wavelet transform. Facial database consists of six basic human emotions including happiness, sadness, anger, surprise, fear and dislike which have been known as common emotions regardless of nation and culture. Emotion recognition in the static images is performed by using the discrete wavelet. Here, the feature vectors are extracted by using PCA. Emotion recognition in the dynamic images is performed by using the wavelet transform and PCA. And then, those are modeled by the HMM. Finally, we obtained better performance result from merging the recognition results for the static images and dynamic images.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.12
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• pp.1816-1825
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• 2022

In this paper, we propose a new approach for tracking each human in a surveillance camera network with various resolution cameras. When tracking human on multiple non-overlapping cameras, the traditional appearance features are easily affected by various camera viewing conditions. To overcome this limitation, the proposed system utilizes facial information along with appearance information. In general, human images captured by the surveillance camera are often low resolution, so it is necessary to be able to extract useful features even from low-resolution faces to facilitate tracking. In the proposed tracking scheme, texture-based face descriptor is exploited to extract features from detected face after face frontalization. In addition, when the size of the face captured by the surveillance camera is very small, a super-resolution technique that enlarges the face is also exploited. The experimental results on the public benchmark Dana36 dataset show promising performance of the proposed algorithm.

• Journal of KIISE:Software and Applications
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• v.35 no.10
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• pp.616-626
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• 2008

The face shape extracted by the depth values has different appearance as the most important facial feature information and the face images decomposed into frequency subband are signified personal features in detail. In this paper, we develop a method for recognizing the range face images by combining the multiple frequency domains for each depth image and depth fusion using fuzzy integral. For the proposed approach, the first step tries to find the nose tip that has a protrusion shape on the face from the extracted face area. It is used as the reference point to normalize for orientated facial pose and extract multiple areas by the depth threshold values. In the second step, we adopt as features for the authentication problem the wavelet coefficient extracted from some wavelet subband to use feature information. The third step of approach concerns the application of eigenface and Linear Discriminant Analysis (LDA) method to reduce the dimension and classify. In the last step, the aggregation of the individual classifiers using the fuzzy integral is explained for extracted coefficient at each resolution level. In the experimental results, using the depth threshold value 60 (DT60) show the highest recognition rate among the regions, and the depth fusion method achieves 98.6% recognition rate, incase of fuzzy integral.