Human face aging based on active appearance model using proper feature set

In this paper, a simulation of the human face evolution from youth to old age is presented. In this work, the goal is to maintain a person's appearance and accurately estimate the face at the target age; in such a way that the simulated face should be realistic and have the appearance and texture characteristics of the target age. To achieve this goal, the number of feature points and their arrangement on the face are very important. First, we suggest proper feature points. Then, using the active appearance model (AAM) method and the presented model, templates are obtained as representative of different age groups. To maintain the unique geometric characteristics of the input face and apply these characteristics to the target age template, the proposed feature point pattern and the MLS method have been used. Then, using the active appearance model method and having the target age template contain the geometric characteristics of the input face, and the steps of changing the input face age to reach the target age are presented. Finally, the results of the survey, using two methods of age recognition and real image recognition (separately for man and woman images), show an average of 80.77% (male images) and 81.36% (female images) correct answers of the participants in this Poll.

Automated summary

This paper presents a simulation of human face evolution from youth to old age. The goal is to accurately estimate a person's face at the target age while maintaining their appearance. The proposed method involves selecting proper feature points, using the active appearance model (AAM) method and templates to represent different age groups. The results of the survey show that the method has a high accuracy in age recognition and real image recognition.