Development Of Human Skin Detection Algorithm Using Multilayer Perceptron Neural Network And Clustering Method

Human skin detection is an important preprocessing step in many applications involving images such as face detection, gesture tracking, and nudity detection. Color is a significant source of information for human skin detection, and some studies have discussed the effect of color space on skin detection. However, there is no consensus on which color space is the most appropriate for skin color detection. In addition, good performance of such applications depends on reliable skin classifiers that must be able to discriminate between skin and non-skin pixels for a wide range of people, regardless of age, gender, or race. Many classifiers including intelligent classifiers have been utilized for human skin detection with a few limitations such as low accuracy. In this work, a comprehensive comparative study using the Multilayer Perceptron Artificial Neural Network (MLP ANN) is performed on various color spaces (RGB, normalized RGB, YCbCr, YIQ, HSV, YUV, YDbDr, and CIE L*a*b) to determine the optimum color space. Additionally, the effect of combining texture information with color information is investigated with the aim of boosting the performance of skin classifiers. The Differential Evolution Algorithm (DE) is used in this work to select the optimum color and texture information to achieve the optimum response. The experimental results show that the YIQ color space yields the highest separability between skin and non-skin pixels among the different color spaces tested using color features. In addition, the results reveal that combining color and texture features leads to more accurate and efficient skin detection. Based on these feature extraction results, a system based on a combination of an MLP ANN and k-means clustering which employs the YIQ color space and the statistical features of human skin as inputs is developed for human skin detection. The performance of the developed system has been compared with the existing intelligent skin detection systems. The experimental results reveal that the developed algorithm is able to achieve an accuracy of 87.82% F1-measure based on images from the ECU database. This result demonstrates that optimum feature selection and combination intelligent system are able to enhance the accuracy and reliability of human skin detection significantly

Study on Students' Experiences About Online Teaching During COVID-19 Outbreak

The online teaching in colleges and universities during the COVID-19 outbreak is one of the challenges faced by faculty and students during this period especially for colleges and universities that meet the quality assurance standards and under the accreditation process. One of the main requirements of quality standards is to carry out a variety of opinion surveys at different stages among different levels of study, analyze, and then provide recommended solutions based on survey findings. Although many researchers have been carried out online teaching, there is no consensus on the impact of a sudden transition from face-to-face learning to online learning especially in community colleges in Saudi Arabia. The purpose of this paper is to present the outcomes of the study on students' experiences about online teaching during COVID-19 Outbreak. Smart PLS program is used for testing the model and to make sure that the variables are appropriate and the outcomes are valid

An optimized skin texture model using gray-level co-occurrence matrix

Texture analysis is devised to address the weakness of color-based image segmentation models by considering the statistical and spatial relations among the group of neighbor pixels in the image instead of relying on color information of individual pixels solely. Due to decent performance of the gray-level co-occurrence matrix (GLCM) in texture analysis of natural objects, this study employs this technique to analyze the human skin texture characteristics. The main goal of this study is to investigate the impact of major GLCM parameters including quantization level, displacement magnitudes, displacement direction and GLCM features on skin segmentation and classification performance. Each of these parameters has been assessed and optimized using an exhaustive supervised search from a fairly large initial feature space. Three supervised classifiers including Random Forest, Support Vector Machine and Multilayer Perceptron have been employed to evaluate the performance of the feature space subsets. Evaluation results using Edith Cowan University (ECU) dataset showed that the proposed texture-assisted skin detection model outperformed pixelwise skin detection by significant margin. The proposed method generates an F-score of 91.98, which is satisfactory, considering the challenging scenario in ECU dataset. Comparison of the proposed texture-assisted skin detection model with some state-of-the-art skin detection models indicates high accuracy and F-score of the proposed model. The findings of this study can be used in various disciplines, such as face recognition, skin disorder and lesion recognition, and nudity detection