Therefore, this component mainly describes face growth before adulthood. One study used 400 subjects between 0 and 50 years old to train a face growth model, which showed that the main component associated with face size changed greatly from 0 to 18 years of age with no significant changes in subjects older than 18 years 18. To date, few studies have used 3D images to analyze human facial aging. Previous studies have tried to estimate human age using 2D facial images 15, 16, 17. As such longitudinal morphology data are unavailable, the most feasible strategy is to collect images of different people from different age groups for facial aging analysis. One caveat of facial aging analysis is the difficulty in collecting facial images of the same individual from young to old stages during the whole lifetime. In the field of face recognition, the main differences in exterior facial structure that make individuals distinguishable from each other allow recognition analysis to achieve relatively high accuracy 14. Thus, large variations exist in facial aging across individuals and ethnic populations. As facial aging is a complex process involving soft tissues and skeleton structures, it is influenced by many factors, such as exposure to sunlight and body weight 12. Using computed tomography scans, researchers observed larger eye sockets, reduced angle of brow and significant angle changes of the lower jaw as people age 13, indicating that facial bone morphology is strongly associated with aging. Other studies have revealed that the loss of facial bone volume also contributes greatly to age-dependent facial alterations.
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Initial reports suggested that skin and soft tissues play important roles in showing signs of facial aging 10, 11, 12. However, very few studies have characterized changes associated with the aging face. For example, it has been extensively used to recognize many dysmorphic syndromes, such as Noonan syndrome, Velocardiofacial syndrome, Williams syndrome, Smith-Magenis syndrome, 22q11 deletion syndrome, Fabry disease, autism spectrum disorders and Wolf-Hirschhorn syndrome 9.įacial aging is one of the most prominent and readily accessible phenotypes of human aging. With the recent development of three-dimensional (3D) imaging technology, such as the widely used stereo photogrammetric camera system 3dMDface System ( and methods to reconstruct 3D images with single-pixel detectors 5, 3D facial data are now used in many fields, such as disease diagnosis and facial morphology comparison between ethnic populations 6, 7, 8. However, despite intense research, so far there is no reliable aging marker to measure the biological age of an individual 4. Reliable prediction of the aging process is not only important for quantitatively assessing the degree of the aging process and its reversal 2, 3, but also important for assessing the risks of aging-associated diseases and for designing individualized treatment schemes 4. Despite a close relationship between facial morphological features and health indicators in the blood, facial features are more reliable aging biomarkers than blood profiles and can better reflect the general health status than chronological age.Īging is a major risk factor for many complex diseases 1. Using this predictor, we identified slow and fast agers that are significantly supported by levels of health indicators. We constructed a robust age predictor and found that on average people of the same chronological age differ by ± 6 years in facial age, with the deviations increasing after age 40. We identified quantitative facial features, such as eye slopes, highly associated with age. By analyzing the morphological profiles, we generated the first comprehensive map of the aging human facial phenome. We collected > 300 3D human facial images and blood profiles well-distributed across ages of 17 to 77 years.
Here we addressed this problem by examining whether human 3D facial imaging features could be used as reliable aging markers. However, despite intense research, so far there is no reliable aging marker. Reliable prediction of the aging process is important for assessing the risks of aging-associated diseases. Aging is associated with many complex diseases.
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