To search age-correlated facial features and construct an age estimation model based on the three-dimensional (3D) facial images of Xinjiang Uygur males, and to structure individual face images of old age and young age. Pretreatment was performed to collect 105 3D facial images of Xingjiang Uygur males aged between 17-57 years by Artec Studio software. The facial images were transferred to high-density 3D dot matrix data by FaceAnalysis software, and each image could be represented with 32 251

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... ertexes. Central correction of the facial images was done and all the data were aligned to a standard coordinate frame by generalized Procrustes analysis (GPA). The age estimation model was established by partial least square regression (PLSR). Furthermore, the changes of age-correlated facial features were presented on the heat map of average face, and the reconstruction of facial images at different ages was performed based on this model. With age, the average faces showed a series of changes including the nasolabial sulcus deepening, cheek sinking, cheekbone protruding and eye corner drooping. The Pearson correlation coefficient (r) between estimated age and chronological age was 0.71. The mean absolute deviation (MAD) of age estimation was 6.37 years. The results of age estimation in >30-40 years group showed a best accuracy (MAD=4.27 years), and the deviations increased with age after 40 years. The composite facial images represented a significant result with age on facial morphological features and aging. The results of this study reveal the age-correlated facial features and aging markers in Uygur population, which help to construct a reliable age estimation model.