Abstract: Face aging process is very complex, and skin of features and purchase is subtle with a lot of noise. The traditional leather channel matching method can't depict subtle aging characteristics change wall, and face aging test accuracy is not strong. A face aging detection algorithm based on a small neighborhood and the characteristics groups was proposed. The corresponding relationship between small face image neighborhood and characteristics groups was set up,and the energy function of tectonic motion parameters association probability distribution was constructed to reflect the constraint relation between the parameters. The simulated annealing algorithm was used to seek the optimal solution of the related constraints association of the skin channel feature point relevance,geting aging characteristics under the different aging condition. I}he experiment shows that this method can recognize the aging skin channel characteristics under the different face state. Test results are more accurate,higher accuracy.

Key words: Face aging, Small neighborhood, Characteristics groups