Multi-feature-Based Facial Age Estimation Using an Incomplete Facial Aging Database

Age estimation from face images is a complex process as it varies from person to person, affected by various intrinsic factors (such as genetic and hormonal) and extrinsic factors (such as environmental, lifestyle, illumination, pose and expression). In this paper, an age estimation system has been proposed that preserves personalized aging trait as well as being robust to change in appearance, shape, wrinkle, texture, expression, pose and illumination of a face. The Golden ratio-based face cropping maintains uniformity of facial regions among faces irrespective of age, gender and race. Local, global and combinational features are extracted to handle the variations of intrinsic and extrinsic factors of aging. The experiment is conducted on FG-NET-AD and MORPH Album-2 facial aging databases. As the existing facial aging databases are incomplete, dealing with strong person-specificity, and high within-range variance; so the dominance of one age-group on other is resolved by feature filling that is carried out by a multi-feature-based modified expectation maximization algorithm. The age estimation is carried out by a three-level hierarchical classifier based on SVM and SVR by choosing suitable combination of hybrid feature sets. The experimental results show the superiority of proposed approach as compared to some of the existing age estimation approaches available in the literature.