Åldersuppskattning med maskininlärning

Sammanfattning: Machine Learning (ML) is a research area in artificial intelligence (AI) and computer science. ML focuses on the use of data and algorithms to identify patterns in data without direct instruction. This is done with the help of ML algorithms that learn to make predictions by finding rules and drawing conclusions based on training data. ML can be used to perform tasks such as estimating human's age based on facial images, which can be used to control or restrict access to a website based on the user's age.Age estimation from facial images can be described as a regression problem or a classification problem. Estimating the exact age is a regression problem, while estimating the age group is a classification problem. A regression problem can be converted to a classification problem to determine the age group from the estimated age. This is done by dividing the total age range into different age groups, after which it is decided which group the age estimate belongs to. This study aims to answer how ML models can be used to estimate different age groups from facial images. This is done by exploring and evaluating two classification models that directly estimate the age group, in comparison with determining the age group from the exact age estimate by converting the regression problem into a classification problem. In this work, facial images are used to train and test ML algorithms by combining facial images from various open research databases. A delimitation was made in this study to only explore the use of Convolutional Neural Networks (CNN) to create different ML models that can estimate the age or the age group. CNN are used to perform tasks that require image interpretation, which in this case means that facial images are interpreted to make predictions. The results show that one of two classification models in this study achieves an accuracy of 75.9%. The second classification model, which estimates other age groups, achieves an accuracy of 62.88%. However, the outcome of two converted classification problems from a regression model shows an accuracy of 68.85% and 70.68%, respectively. The estimation model that achieves the highest accuracy when estimating the age group is a classification model with 75.90% accuracy. The work indicates that the choice of age group interval and facial images within each age group determine how the estimation models perform in relation to each other.