Facial aging, a new dimension that has recently been added to the problem of face recognition, poses interesting theoretical and practical challenges to the research community . How do humans perceive age ? What constitutes an age-invariant signature for faces ? How do we model facial growth across different ages ? How does facial aging effects impact recognition performance ? This thesis provides a thorough overview of the problem of facial aging and addresses the aforementioned questions.

We propose a craniofacial growth model that characterizes growth related shape variations observed in human faces during formative years (0 - 18 yrs). The craniofacial growth model draws inspiration from the `revised' cardioidal strain transformation model proposed in psychophysics and further, incorporates age-based anthropometric evidences collected on facial growth during formative years. Identifying a set of fiducial features on faces, we characterize facial growth by means of growth parameters estimated on the fiducial features. We illustrate how the growth related transformations observed on facial proportions can be studied by means of linear and non-linear equations in facial growth parameters, which subsequently help in computing the growth parameters. The proposed growth model implicitly accounts for factors such as gender, ethnicity, the individual's age group etc. Predicting one's appearance across ages, performing face verification across ages etc. are some of the intended applications of the model.

Next, we propose a two-fold approach towards modeling facial aging in adults. Firstly, we develop a shape transformation model that is formulated as a physically-based parametric muscle model that captures the subtle deformations facial features undergo with age. The model implicitly accounts for the physical properties and geometric orientations of the individual facial muscles. Next, we develop an image gradient based texture transformation function that characterizes facial wrinkles and other skin artifacts often observed during different ages. Facial growth statistics (both in terms of shape and texture) play a crucial role in developing the aforementioned transformation models. From a database that comprises of pairs of age separated face images of many individuals, we extract age-based facial measurements across key fiducial features and further, study textural variations across ages. We present experimental results that illustrate the applications of the proposed facial aging model in tasks such as face verification and facial appearance prediction across aging.

How sensitive are face verification systems to facial aging effects ? How does age progression affect the similarity between a pair of face images of an individual ? We develop a Bayesian age difference classifier that classifies face images of individuals based on age differences and performs face verification across age progression. Further, we study the similarity of faces across age progression. Since age separated face images invariably differ in illumination and pose, we propose pre-processing methods for minimizing such variations. Experimental results using a database comprising of pairs of face images that were retrieved from the passports of 465 individuals are presented. The verification system for faces separated by as many as 9 years, attains an equal error rate of 8.5%.