People's fatness and thinness detection using image processing and machine learning

One of the most important priorities in developed countries is the use of machine decision-making instead of a human. One of the areas that need this field is health. For this purpose, determining the obesity and thinness of people can be very useful in studying and examining the health status of a society and adopting health system policies. Images of people as a database of research have been prepared from several different environments where the distance between the camera and the person is the same in all of them. Then, the background of the image is removed using background subtraction. Image features that include image morphological characteristics are extracted from the image and are classified into two categories to perform classification operations. The people were divided into three categories: fat, medium, and thin. The images are noised using the Gaussian low pass filter method with different frequencies filtered using two methods of salt and pepper noise and Gaussian noise. n normal images, the highest accuracy is related to the support vector machine method with an accuracy of 91.7%The results of this paper showed that with the proposed method, in addition to being able to classify the people of a society in terms of obesity and thinness, a higher accuracy was achieved than most of the methods that have been presented so far. According to the solutions and results of this research, by increasing the images of people, in addition to increasing the accuracy, it will reach a more practical level.