• Journal of Genetic Medicine
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• 제10권1호
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• pp.57-61
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• 2013

Acromegaloid Facial Appearance syndrome is a very rare syndrome combining acromegaloid-like facial appearance, thickened lips and oral mucosa and acral enlargement. Progressive facial dysmorphism is characterized by a coarse facies, a long bulbous nose, high-arched eyebrows, and thickening of the lips, oral mucosa leading to exaggerated rugae and frenula, furrowed tongue and narrow palpebral fissures. We report a case of acromegaloid facial appearance syndrome in a 19-year-old male patient who presented with all the characteristic features of the syndrome along with previously unreported anomalies like dystrophic nails, postaxial polydactyly and incisal notching of teeth.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 한국해양정보통신학회 1999년도 추계종합학술대회
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• pp.416-419
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• 1999

We Present a technique for recognizing facial expressions from image sequences. The technique uses muscle-based feature models for tracking facial features. Since the feature models are constructed with a small number of parameters and are deformable in the limited range and directions, each search space for a feature can be limited. The technique estimates muscular contractile degrees for classifying six principal facial express expressions. The contractile vectors are obtained from the deformations of facial muscle models. Similarities are defined between those vectors and representative vectors of principal expressions and are used for determining facial expressions.

• Journal of the Korean Academy of Child and Adolescent Psychiatry
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• 제23권1호
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• pp.31-35
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• 2012

Noonan syndrome is characterized by short stature, typical facial dysmorphology, and congenital heart defects. The main facial features of Noonan syndrome are hypertelorism with down-slanting palpebral fissures, ptosis, and low-set posteriorly-rotated ears with a thickened helix. The cardiovascular defects most commonly associated with this condition are pulmonary stenosis and hypertrophic cardiomyopathy. Other associated features are webbed neck, chest deformity, mild intellectual deficit, cryptorchidism, poor feeding in infancy, bleeding tendency, and lymphatic dysplasias. The patient is a 10-year-old boy. He had experienced repeated febrile convulsions. He had typical facial features, a short stature, chest deformity, cryptorchidism, vesicoureteral reflux, and mental retardation. His language and motor development were delayed. When he went to school, it was difficult for him to pay attention, follow directions, and organize tasks. He also displayed behavior such as squirming, leaving his seat in class, and running around inappropriately. Clinical observation is important for the diagnosis, so we report a patient who was diagnosed with Noonan syndrome, mental retardation, and attention-deficit hyperactivity disorder.

• International Journal of Computer Science & Network Security
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• 제22권6호
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• pp.319-331
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• 2022

The latest global COVID-19 pandemic has made the use of facial masks an important aspect of our lives. People are advised to cover their faces in public spaces to discourage illness from spreading. Using these face masks posed a significant concern about the exactness of the face identification method used to search and unlock telephones at the school/office. Many companies have already built the requisite data in-house to incorporate such a scheme, using face recognition as an authentication. Unfortunately, veiled faces hinder the detection and acknowledgment of these facial identity schemes and seek to invalidate the internal data collection. Biometric systems that use the face as authentication cause problems with detection or recognition (face or persons). In this research, a novel model has been developed to detect and recognize faces and persons for authentication using scale invariant features (SIFT) for the whole segmented face with an efficient local binary texture features (DLBP) in region of eyes in the masked face. The Fuzzy C means is utilized to segment the image. These mixed features are trained significantly in a convolution neural network (CNN) model. The main advantage of this model is that can detect and recognizing faces by assigning weights to the selected features aimed to grant or provoke permissions with high accuracy.

• Annals of Clinical Neurophysiology
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• 제10권1호
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• pp.38-42
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• 2008

Background: Idiopathic facial nerve palsy, or Bell's palsy (BP), is a common and important disease. Recurrent Bell's palsy has been known as a rare entity with only a few cases in the literature. Methods: A total of 111 consecutive patients with acute BP patients were enrolled at Daegu Catholic University Hospital from July 2005 to March 2007. We classified the patients into two groups - single BP and recurrent BP - and compared them by demographic data, clinical features, MRI findings and prognosis. The degree of BP was graded according to the House and Brackmann facial nerve grading system. Results: Recurrent BP was observed in 10 (9%) patients. The number of recurrence was varied from 2 to 5. The mean age of first attack in recurrent BP was $35.70{\pm}23.65$ years old and was earlier than that of the single BP ($50.94{\pm}16.21$ year). The larger proportion of the single BP had an abnormal enhancement of affected facial nerve (91.3%) than the recurrent BP (50%). The recurrent BP showed worse prognosis than the single BP. The associated conditions, etiology, and clinical features were similar between two groups. Conclusions: In comparison with single BP, recurrent BP showed earlier onset of first BP attack, less frequent abnormal enhancement of facial nerve on MRI, and worse prognosis.

• Journal of Institute of Control, Robotics and Systems
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• 제19권6호
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• pp.513-519
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• 2013

The current vision-based approaches for emotion recognition, such as facial expression analysis, have many technical limitations in real circumstances, and are not suitable for applications that use them solely in practical environments. In this paper, we propose an approach for emotion recognition by combining extrinsic representations and intrinsic activities among the natural responses of humans which are given specific imuli for inducing emotional states. The intrinsic activities can be used to compensate the uncertainty of extrinsic representations of emotional states. This combination is done by using PRMs (Probabilistic Relational Models) which are extent version of bayesian networks and are learned by greedy-search algorithms and expectation-maximization algorithms. Previous research of facial expression-related extrinsic emotion features and physiological signal-based intrinsic emotion features are combined into the attributes of the PRMs in the emotion recognition domain. The maximum likelihood estimation with the given dependency structure and estimated parameter set is used to classify the label of the target emotional states.

• Archives of Plastic Surgery
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• 제42권5호
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• pp.567-571
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• 2015

Background Bariatric surgeries performed in the USA has increased twelve-fold in the past two decades. The effects of rapid weight loss on facial features has not been previously studied. We hypothesized that bariatric surgery will mimic the effects of aging thus giving the patient an older and less attractive appearance. Methods Consecutive patients were enrolled from the bariatric surgical clinic at our institution. Pre and post weight loss photographs were taken and used to generate two surveys. The surveys were distributed through social media to assess the difference between the preoperative and postoperative facial photos, in terms of patients' perceived age and overall attractiveness. 102 respondents completed the first survey and 95 respondents completed the second survey. Results Of the 14 patients, five showed statistically significant change in perceived age (three more likely to be perceived older and two less likely to be perceived older). The patients were assessed to be more attractive postoperatively, which showed statistical significance. Conclusions Weight loss does affect facial aesthetics. Mild weight loss is perceived by survey respondents to give the appearance of a younger but less attractive patient, while substantial weight loss is perceived to give the appearance of an older but more attractive patient.

• Journal of the Korea Institute of Information and Communication Engineering
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• 제8권6호
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• pp.1182-1187
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• 2004

Robust, real-time, fully automatic tracking of facial features is required for many computer vision and graphics applications. In this paper, we describe a fully automatic system that tracks eyes and eyebrows in real time. The pupils are tracked using the red eye effect by an infrared sensitive camera equipped with infrared LEDs. Templates are used to parameterize the facial features. For each new frame, the pupil coordinates are used to extract cropped images of eyes and eyebrows. The template parameters are recovered by PCA analysis on these extracted images using a PCA basis, which was constructed during the training phase with some example images. The system runs at 30 fps and requires no manual initialization or calibration. The system is shown to work well on sequences with considerable head motions and occlusions.

• International Journal of Advanced Culture Technology
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• 제5권1호
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• pp.51-57
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• 2017

Digital signage is becoming part of daily life across a wide range of visual advertisements segments market used in stations, hotels, retail stores, hotels, etc. The current digital signage system used in market is generally works on limited user interactivity with static contents. In this paper, a new approach is proposed using computer vision based dynamic audience adaptive cost-effective digital signage system. The proposed design uses the Camera attached Raspberry Pi Open source platform to employ the real-time audience interaction using computer vision algorithms to extract facial features of the audience. The real-time facial features are extracted using Haar Cascade algorithm which are used for audience gender specific rendering of dynamic digital signage content. The audience facial characterization using Haar Cascade is evaluated on the FERET database with 95% accuracy for gender classification. The proposed system, developed and evaluated with male and female audiences in real-life environments camera embedded raspberry pi with good level of accuracy.

• Journal of the Korea Convergence Society
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• 제9권4호
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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.