In order to handle image noise as well as varying facial expressions, a nonlinear appearance model called kernel appearance model (KAM) is derived. Initially using active appearance models (AAM), facial features are extracted and mapped to people’s ages, afterward a formula is derived which allows the convenient generation of age progressed images irrespective of whether the intended age exists in the training database or not. To this end, this thesis investigates the problem with a view to tackling the most prominent issues associated with the existing algorithms. Furthermore, most of the algorithms use a pattern caricaturing approach which infers ages by manipulating the target image and a template face formed by averaging faces at the intended age. Although many algorithms have been proposed, most of the methods are affected by image noise, illumination variations, and facial expressions. Among these is the frequent search for missing people, in the UK alone up to 300,000 people are reported missing every year. Abstract Recently, automatic age progression has gained popularity due to its numerous applications.
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