1,722,258 research outputs found
Trends and Risk Factors of Metabolic Syndrome among Korean Adolescents, 2007 to 2018 (Diabetes Metab J 2021;45:880-9)
Get PDFNatural facial expression recognition using differential-AAM and manifold learning
No full textThis paper proposes a novel natural facial expression recognition method that recognizes a sequence of dynamic facial expression images using the differential active appearance model (AAM) and manifold learning as follows. First, the differential-AAM features (DAFs) are computed by the difference of the AAM parameters between an input face image and a reference (neutral expression) face image. Second, manifold learning embeds the DAFs on the smooth and continuous feature space. Third, the input facial expression is recognized through two steps: (1) computing the distances between the input image sequence and gallery image sequences using directed Hausdorff distance (DHD) and (2) selecting the expression by a majority voting of k-nearest neighbors (k-NN) sequences in the gallery. The DAFs are robust and efficient for the facial expression analysis due to the elimination of the inter-person, camera, and illumination variations. Since the DAFs treat the neutral expression image as the reference image, the neutral expression image must be found effectively. This is done via the differential facial expression probability density model (DFEPDM) using the kernel density approximation of the positively directional DAFs changing from neutral to angry (happy, surprised) and negatively directional DAFs changing from angry (happy, surprised) to neutral. Then, a face image is considered to be the neutral expression if it has the maximum DFEPDM in the input sequences. Experimental results show that (1) the DAFs improve the facial expression recognition performance over conventional AAM features by 20% and (2) the sequence-based k-NN classifier provides a 95% facial expression recognition performance on the facial expression database (FED06). (C) 2008 Elsevier Ltd. All rights reserved.X1172sciescopu
Pose invariant face recognition using linear pose transformation in feature space
No full textRecognizing human face is one of the most important part in biometrics. However, drastic change of facial pose makes it a difficult problem. In this paper, we propose linear pose transformation method in feature space. At first, we extracted features from input face image at each pose. Then, we used extracted features to transform an input pose image into its corresponding frontal pose image. The experimental results show that recognition rate with pose transformation is much better than the result without pose transformation.X11sciescopu
Start Early, Do It Well: Implications of a National Diabetes Care Quality Assessment Program for Life Expectancy
No full textForecasting Time Series with Genetic Fuzzy Predictor Ensemble
No full textThis paper proposes a genetic fuzzy predictor ensemble (GFPE) for the accurate prediction of the future in the chaotic or nonstationary time series. Each fuzzy predictor in the GFPE is built from two design stages, where each stage is performed by different genetic algorithms (GA's). The first stage generates a fuzzy rule base that covers as many of training examples as possible. The second stage builds fine-tuned membership functions that make the prediction error as small as possible. These two design stages are repeated independently upon the different partition combinations of input-output variables. The prediction error will be reduced further by invoking the GFPE that combines multiple fuzzy predictors by an equal prediction error weighting method. Applications to both the Mackey-Glass chaotic time Series and the nonstationary foreign currency exchange rate prediction problem are presented. The prediction accuracy of the proposed method is compared with that of other fuzzy and neural network predictors in terms of the root mean squared error (RMSE).X11158sciescopu
Robust face tracking by integration of two separate trackers: Skin color and facial shape
No full textThis paper proposes a robust face tracking method based on the condensation algorithm that uses skin color and facial shape as observation measures. Two trackers are used for robust tracking: one tracks the skin color regions and the other tracks the facial shape regions. The two trackers are coupled using an importance sampling technique, where the skin color density obtained from the skin color tracker is used as the importance function to generate samples for the shape tracker. The samples of the skin color tracker within the chosen shape region are updated with higher weights. Also, an adaptive color model is used to avoid the effect of illumination change in the skin color tracker. The proposed face tracker performs more robustly than either the skin-color-based tracker or the facial shape-based tracker, given the presence of background clutter and/or illumination changes. (c) 2007 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.X117sciescopu
Changes in the Epidemiological Landscape of Diabetes in South Korea: Trends in Prevalence, Incidence, and Healthcare Expenditures
Get PDFDiabetes is a serious public health concern that significantly contributes to the global burden of disease. In Korea, the prevalence of diabetes is 12.5% among individuals aged 19 and older, and 14.8% among individuals aged 30 and older as of 2022. The total number of people with diabetes among those aged 19 and older is estimated to be 5.4 million. The incidence of diabetes decreased from 8.1 per 1,000 persons in 2006 to 6.3 per 1,000 persons in 2014, before rising again to 7.5 per 1,000 persons in 2019. Meanwhile, the incidence of type 1 diabetes increased significantly, from 1.1 per 100,000 persons in 1995 to 4.8 per 100,000 persons in 2016, with the prevalence reaching 41.0 per 100,000 persons in 2017. Additionally, the prevalence of gestational diabetes saw a substantial rise from 4.1% in 2007 to 22.3% in 2023. These changes have resulted in increases in the total medical costs for diabetes, covering both outpatient and inpatient services. Therefore, effective diabetes prevention strategies are urgently needed
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