A New Method for Estimating Body Height from Uncalibrated Images

We address the problem of estimating a person’s body height from a single uncalibrated image. The novelty of our work lies in that we handle two difficult cases not previously addressed in the literature: (i) the image contains no reference length in the background scene, (ii) the image contains the upper body only. In a nutshell, our method combines well-known ideas from projective geometry and single-view metrology with prior probabilistic/statistical knowledge of human anthropometry, in a Bayesian-like framework. The method is demonstrated with synthetic (randomly generated) data as well as a dataset of 96 frontal and 12 nonfrontal images, and the results are quite encouraging.