Date of Award
Thesis (Campus Access Only)
Master of Science
Electrical and Computer Engr
Aaron L. Robinson
Estimation of various anthropometric measurements and body weight is an important problem in medical and forensic domains. In this thesis, we investigate solutions for the challenging problem of weight and height estimation from images and videos, using which we calculate the body mass index (BMI). The proposed approach consists of two steps. First, the measurements of height, chest, waist and hip are obtained from images/videos using a suite of computer vision techniques while the age and gender are assumed to be given. Next, a model is trained to extract the relations between these six variables and the weight, which is then used for inference. A number of models are considered, namely, Gaussian mixture regression, principal component regression, random forest regression, generalized regression neural network, multilayered perceptron, independent component regression, clustering, sparse coding, deep belief network, and copula regression. Performances of the models are compared using root mean square error and correlation coefficient between inferred and actual weights. For data collected by our collaborators as well as other publicly available datasets, a few of the models clearly outperform the rest.
Dissertation or thesis originally submitted to the local University of Memphis Electronic Theses & dissertation (ETD) Repository.
Manam, Vineesha, "Estimation of body mass index using images and videos" (2016). Electronic Theses and Dissertations. 2322.