Measuring Food Volume and Nutritional Values from Food Images

Abstract

Obesity and being overweight have become growing concerns due to their association with many diseases, such as type II diabetes, several types of cancer and heart disease. Thus, obesity treatments have been the focus of a large number of recent studies. Because of these studies, researchers have found that the treatment of obesity and being overweight requires constant monitoring of the patient’s diet. Therefore, measuring food intake each day is considered an important step in the success of a healthy diet. Measuring daily food consumption for obese patients is one of the challenges in obesity management studies. Countless recent studies have suggested that using technology like smartphones may enhance the under-reporting issue in dietary intake consumption. In this thesis, we propose a Food Recognition System (FRS) for calories and nutrient values assumption. The user employs the built-in camera of the smartphone to take a picture of any food before and after eating. The system then processes and classifies the images to detect the type of food and portion size, then uses the information to estimate the number of calories in the food. The estimation and calculation of the food volume and amount of calories in the image is an essential step in our system. Via special approaches, the FRS can estimate the food volume and the existing calories with a high level of accuracy. Our experiment shows high reliability and accuracy of this approach, with less than 15% error.