• Journal of KIISE
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• v.41 no.9
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• pp.666-673
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• 2014

This paper proposes a scalable face recognition method for unconstrained face databases, and shows a simple experimental result. Existing face recognition research usually has focused on improving the recognition rate in a constrained environment where illumination, face alignment, facial expression, and background is controlled. Therefore, it cannot be applied in unconstrained face databases. The proposed system is face feature extraction algorithm for unconstrained face recognition. First of all, we extract the area that represent the important features(landmarks) in the face, like the eyes, nose, and mouth. Each landmark is represented by a high-dimensional LBP(Local Binary Pattern) histogram feature vector. The multi-scale LBP histogram vector corresponding to a single landmark, becomes a low-dimensional face feature vector through the feature reduction process, PCA(Principal Component Analysis) and LDA(Linear Discriminant Analysis). We use the Rank acquisition method and Precision at k(p@k) performance verification method for verifying the face recognition performance of the low-dimensional face feature by the proposed algorithm. To generate the experimental results of face recognition we used the FERET, LFW and PubFig83 database. The face recognition system using the proposed algorithm showed a better classification performance over the existing methods.

• Archives of Craniofacial Surgery
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• v.17 no.3
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• pp.173-175
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• 2016

Chondroid syringoma is a rare mixed tumor of the skin which is composed of both mesenchymal and epithelial cells. Its incidence at less than 0.1% and is frequently located on the head and neck. Chondroid syringoma is easily confused with epidermal cysts. Since malignant forms of chondroid syringoma have been reported, accurate and timely diagnosis is important for proper management. We report clinical and histological features of chondroid syringoma in 5 patients treated at our institution. In most of the cases, chondroid syringoma presented as a round, firm, nodular or cystic lesion that had well marginated heterogeneity in sonography. Clinically, all of the lesions were removed by simple excision. Microscopically, all five tumors were well circumscribed and consisted of epithelial, myoepithelial, and stromal components. The epithelial component formed tubules lined by one or more rows of eosinophilic epithelial cells. The outer layer of tubules appeared to be flattened myoepithelial cells. The stroma is myxoid and contained spindle shaped myoepithelial cells. We expect that the clinical, sonographic, and histological data from our report may help clinicians who are confronted with various kinds of analogous facial lesions to decide the most proper management for their patients.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.41 no.6
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• pp.155-164
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• 2004

Recently, we have remarkable developments in intelligent robot systems. The remarkable features of intelligent robot are that it can track user and is able to doface recognition, which is vital for many surveillance-based systems. The advantage of face recognition compared with other biometrics recognition is that coerciveness and contact that usually exist when we acquire characteristics do not exist in face recognition. However, the accuracy of face recognition is lower than other biometric recognition due to the decreasing in dimension from image acquisition step and various changes associated with face pose and background. There are many factors that deteriorate performance of face recognition such as thedistance from camera to the face, changes in lighting, pose change, and change of facial expression. In this paper, we implement a new sliding active camera system to prevent various pose variation that influence face recognition performance andacquired frontal face images using PCA and HMM method to improve the face recognition. This proposed face recognition algorithm can be used for intelligent surveillance system and mobile robot system.

• Childhood Kidney Diseases
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• v.8 no.1
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• pp.57-62
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• 2004

Schinzel-Giedion syndrome is a rare, distinct dysmorphic syndrome characterized by congenital hydronephrosis, skeletal dysplasia, and severe developmental retardation, likely to be inherited as an autosomal recessive trait, but not yet confirmed. This syndrome is characterized by coarse facial features such as midfacial retraction, bulging forehead, short nose with anteverted nostrils, low-set malformed ears, protruding large tongue, and hypertelorism. Skeletal and limb defects, choanal stenosis, simian creases, hypospadias, microphallus, hypertrichosis, and intractable seizures are the frequently associated clinical findings. Urogenital involvement is a major component of the syndrome, and this problem sometimes is associated with nephrocalcinosis and urinary tract infection in the clinical course of the disease. We report a 22 month-old girl with Schinzel-Giedion syndrome complicated by medullary nephrocalcinosis and urinary tract infection due to Klebsiella pneumoniae. This patient had also been suffering from postnatal growth deficiency, intractable seizure, spastic tetraplegia, delayed development and severe mental retardation.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.5
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• pp.581-587
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• 2005

This paper presents a hybrid face recognition method of both the first moment of image and the independent component analysis(ICA) of fixed point(FP) algorithm based on Newton method. First moment is a method for finding centroid of image, which is applied to exclude the needless backgrounds in the face recognitions by shifting to the centroid of face image. FP-ICA is also applied to find a set of independent basis images for the faces, which is a set of statistically independent facial features. The proposed method has been applied to the problem for recognizing the 48 face images(12 persons o 4 scenes) of 64*64 pixels. The 3 distances such as city-block, Euclidean, negative angle are used as measures when match the probe images to the nearest gallery images. The experimental results show that the proposed method has a superior recognition performances(speed, rate) than conventional FP-ICA without preprocessing. The city-block has been relatively achieved more an accurate similarity than Euclidean or negative angle.

• Journal of Korea Multimedia Society
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• v.11 no.11
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• pp.1501-1514
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• 2008

The face shape extracted by the depth values has different appearance as the most important facial information. 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 multiple frequency domains for each depth image using the modified fuzzy c-mean algorithm. 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. And the second step takes into consideration of the orientated frontal posture to normalize. Multiple contour line areas which have a different shape for each person are extracted by the depth threshold values from the reference point, nose tip. And then, the frequency component extracted from the wavelet subband can be adopted as feature information for the authentication problems. The third step of approach concerns the application of eigenface to reduce the dimension. And the linear discriminant analysis (LDA) method to improve the classification ability between the similar features is adapted. In the last step, the individual classifiers using the modified fuzzy c-mean method based on the K-NN to initialize the membership degree is explained for extracted coefficient at each resolution level. In the experimental results, using the depth threshold value 60 (DT60) showed the highest recognition rate among the extracted regions, and the proposed classification method achieved 98.3% recognition rate, incase of fuzzy cluster.