Search

The primary goal of this paper is to describe a proposed framework for identifying human face expressions. A methodology has been proposed and developed to identify facial emotions using an axes-angular feature extracted from facial landmarks for 4D dynamic facial expression video data. The 4D facial expression recognition (FER) problem is modeled as an unbalanced problem using the full video sequence. The proposed dataset includes landmarks that are positioned to be fiducial features: around the brows, eyes, nose, cheeks, and lips. Following the initial facial landmark preprocessing, feature extraction is carried out. Input feature vectors from gamma axes and magnitudes in three-dimensional Euclidean space are constructed. This paper develops a new feature vector by concatenating the angle and magnitude features and compares them to a model created independently using angle features. In all three models, several filter-based feature selection techniques are used to estimate feature importance. The framework employs a multi-class support vector machine (SVM) on MATLAB, and the true positive rate (TPR) and average recognition rates (ARR) are used as performance metrics. In terms of average classification accuracy, the final results are compared to conventional state-of-the-art approaches. The results revealed a highly informative combined feature, demonstrating the efficiency of the proposed landmark-based strategy to classify facial expressions. [ABSTRACT FROM AUTHOR]

Introduction: wide regional variation in immunization coverage still persists in Nigeria. Full Immunization Coverage (FIC) for more than 80% of all states in the northern region is lower than 40% relative to their southern counterpart. Studies focusing on young women in the north remain sparse, despite the high prevalence of early marriage and poor health-seeking behavior. This study examines FIC among young women in northern Nigeria. Methods: we performed a secondary analysis of the 2013 and 2018 Nigeria Demographic and Health Survey on 1,198 women of children aged 12-23 months in 2013 and 405 in the 2018 dataset. Analysis was limited to young women 15-24 years, residing in Northern Nigeria. We used logistics regression to predict factors associated with FIC. Results: the proportion of fully immunized children was low, at 11% in 2013 and 18% in 2018. The coverage for most vaccines was low, except for the oral polio vaccine. The children of mothers who had health card [(aOR=18.1,95% C.I (8.1-40.7)], in 2013 and 2018 [(aOR=12.7, 95% C.I (5.9-27.1)], attended ANC [(aOR=8.6, 95% C.I (2.4-30.9)] in 2013 and had facility delivery [(aOR=2.0, 95% C.I (1.0-4.1)] in 2018 were more likely to be fully immunized. Conclusion: the study found FIC among children of young women in Northern Nigeria was abysmally low. Ownership of health care, antenatal attendance, and facility delivery significantly predicted the odds of FIC. These findings suggest the need for approaches that remove barriers to good health-seeking behavior, especially among young mothers in Northern Nigeria. [ABSTRACT FROM AUTHOR]