Abstract. This paper presents a model of detection age which quantifies the appearance changes of human skin at different stages of aging without traditional skeleton. We provide a novel method to extract age-related primitive features from skin surface images obtained by a digital camera, including skin surface heterogeneity parameters of the standard deviation of gray (Std) and the information entropy (Entropy) of skin images, the skin surface texture variations parameters of the mean area of the grids (MA) and the number of intersections (NI). Skin surface images of 97 individuals have been analyzed by our image processing program. The results show that the four parameters were statistically correlated with age, indicating their potential for the routine evaluation of skin aging.

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