Abstarct: Abstract In this thesis, a simulation of the evolutionary process of human facial aging from youth to old age is presented. The appearance of facial changes due to aging is influenced by various factors such as genetics, ethnicity, and lifestyle. However, individuals' faces within a specific age group share similar characteristics. These characteristics can be utilized to estimate an individual's facial image in past or future years. In the facial aging model, the geometric changes are minimal, primarily occurring in the facial tissue. Most of these changes involve the loss of muscle-skeletal mass, which happens as a result of aging. The problem of facial age progression from one age to a target age has applications in forensic medicine, criminal investigations, finding missing persons, modeling suspect faces, computer graphics, film industry, cosmetic surgery, and high-risk lifestyle prevention. The goal is to preserve the individual's appearance and accurately estimate the facial features at the target age. The simulated faces should be realistic and possess the appearance of the target age group's tissue characteristics. To achieve this goal, the number and arrangement of facial landmarks are crucial. In this study, we first propose suitable facial landmarks and utilize the Active Appearance Model (AAM) to extract appropriate landmarks from input images for better facial image representation. Then, using the AAM and the proposed landmark configuration, suitable templates are obtained for each of the ten proposed age groups, categorized by gender (male/female). Now, the template representing the desired age group is selected baxsed on the facial image and target age in the output. Unique geometric features of the input face, representing the individual's identity (e.g., facial contour model, elongation, roundness, ovality, and the shape of the eyes, nose, etc.), are applied to this template. For this purpose, the proposed feature point arrangement pattern and the Moving Least Squares (MLS) deformation method are employed. Subsequently, by utilizing the Active Appearance Model and having the target age template containing the unique geometric characteristics of the input face, the stages of changing the age of the input face to reach the target age are presented. Finally, the survey results, using both age detection and real image recognition methods (separately for male and female images), indicate an average of 80.77% correct responses for male images and 81.36% correct responses for female images among the participants in this survey.