Abstract: Age progression results considerable change in the look of human countenances has recently gained attention from the computer vision. Because of numerous ways of life elements, it is hard to decisively anticipate how people may look with propelling years or what they looked like with "withdrawing" years. Automatic age variation methods and techniques useful to capture wanted fugitives, finding missing children, updating employee databases, enhance powerful visual effect in film, television, gaming field. Currently there are many different methods available for age variation like: Craniofacial Growth Model, Anthropometric Model, Image Morphing, Image Based Surface Detail Transfer (IBSDT), Aging function (AGES), Gaussian Mixture Model (GMM) etc. Each method has its own advantages, purpose and limitations. The main goal of our dissertation work is to enhance the effect of age variation for adult people doing facial shape changes and including specific texture information like fine line, wrinkles in age modelling process.
Keywords: Age progression, Age Variation Methods, Anthropometric Model, Ages, Image Morphing, Ibsdt, Gmm.