This paper presents a theory and practical computations for visual age classification from facial images. Currently, the theory has only been implemented to classify input images into one of three agegroups: babies, young adults, and senior adults. The computations are based on cranio-facial development theory and skin wrinkle analysis. In the implementation, primary features of the face are found first, followed by secondary feature analysis. The primary features are the eyes, nose, mouth, chin, virtual-top of the head and the sides of the face. From these features, ratios that distinguish babies from young adults and seniors are computed. In secondary feature analysis, a wrinkle geography map is used to guide the detection and measurement of wrinkles. The wrinkle index computed is sufficient to distinguish seniors from young adults and babies. A combination rule for the ratios and the wrinkle index thus permits categorization of a face into one of three classes. Results using real images are presented. This is the first work involving age classification, and the first work that successfully extracts and uses natural wrinkles. It is also a successful demonstration that facial features are sufficient for a classification task, a finding that is important to the debate about what are appropriate representations for facial analysis.
Kwon and da Vitoria Lobo REF proposed an age classification method based on well-controlled high-quality face images, which can classify faces into one of the three groups (babies, young adults, and senior adults).
Age Classification from Facial Images
Computer Vision and Image Understanding
Computer Science Mathematics