An appearance of a human face changes due to aging: sagging, spots, lusters, and wrinkles would be observed. Therefore, facial aging simulation techniques are required for long-term criminal investigation. While the appearance of an aged face varies greatly from person to person, wrinkles are one of the most important features which represent the human individuality. An individuali-ty of wrinkles is defined by wrinkles shape and position. [Maejima et al. 2014] proposed an aging simulation method that preserves facial parts individuality using a patch-based facial image reconstruction. Since few wrinkles can be observed on an input young face, it is difficult to represent wrinkles appearance only by the reconstruction. Therefore, a statistical wrinkles aging pattern model is introduced to produce natural looking wrinkles by selecting appropriate patches in an age-specific patch database. However, the variation of the statistical wrinkles patterns model is too limited to represent wrinkles individuality. Additionally, an appropriate size of a patch and feature value had to be applied for each facial region to get a plausible aged facial image. In this paper, we introduce a novel aging simulation method us-ing the patch-based image reconstruction, which can overcome problems mentioned above. Based on a medical knowledge [Piér-ard et al. 2003], wrinkles in an expressive facial image (defined as expressive wrinkles) of a same person are synthesized to the input image instead of the statistical wrinkles pattern model to represent wrinkles individuality. Furthermore, different sizes of the patch and feature values are applied for each facial region to achieve high representation of both the wrinkles individuality and the age-specific features. The entire process is performed automatically, and a plausible aged facial image is generated.