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We study the association between physical appearance and family income. In most previous studies, physical appearance was measured by imperfect proxies from subjective opinion based on surveys. Instead, we use the CAESAR data which have 3-dimensional whole body scans to mitigate the issue of possible reporting errors and measurement errors. We show there are significant reporting errors in the reported height and weight so that these discrete measurements are too sparse to provide a complete description of the body shape. We use the graphical autoencoder built on deep machine learning to obtain intrinsic features consisting of human body shapes and estimate the relation between body shapes and family income. The estimation results show that there is a statistically significant relationship between physical appearance and family income and the association are different across the gender. This supports the hypothesis on the physical attractiveness premium in the labor market outcomes and its heterogeneity across the gender. Our findings also highlight the importance of correctly measuring body shapes to provide adequate public policies for the healthcare.