Altinigne, Can YilmazThanou, DorinaAchanta, Radhakrishna2021-03-262021-03-262021-03-262020-01-0110.1109/ICASSP40776.2020.9053363https://infoscience.epfl.ch/handle/20.500.14299/176197WOS:000615970402108We address the difficult problem of estimating the attributes of weight and height of individuals from pictures taken in completely unconstrained settings. We present a deep learning scheme that relies on simultaneous prediction of human silhouettes and skeletal joints as strong regularizers that improve the prediction of attributes such as height and weight. Apart from imparting robustness to the prediction of attributes, our regularization also allows for better visual interpretability of the attribute prediction. For height estimation, our method shows lower mean average error compared to the state of the art despite using a simpler approach. For weight estimation, which has hardly been addressed in the literature, we set a new benchmark.AcousticsEngineering, Electrical & ElectronicEngineeringbiometricsdeep learningheight and weight predictionskeletal joint predictionsegmentationinterpretabilityregularizationstaturetext::conference output::conference proceedings::conference paper