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Measuring human height with a calibrated camera. Calibration provides the relationship between the image and world coordinate systems. Facial key points fall on rays from the camera center and corresponding images of the points. Anthropometric data, conditioned by the estimated age and gender, provides distributions on distances between key points to infer distance from subject to camera, height, age, and gender.
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Abstract
One important problem in computer vision is to provide a demographic description a person from an image. In practice, many of the state-of-the-art methods use only an analysis of the face to estimate the age and gender of a person of interest. We present a model that combines two problems, height estimation and demographic classification, which allows each to serve as context for the other. Our idea is to use a calibrated camera for measuring the height of people in the scene. Height is measured by jointly inferring across anthropometric dimensions, age, and gender using publicly available statistics. The height estimate provides context for recognizing the age and gender of the subject, and likewise age and gender conditions the distribution of the anthropometric features for estimating height.
The performance of our method is explored on a new database of 127 people captured with a calibrated camera with recorded height, age, and gender. We show that estimating height leads to improvements in age and gender classification, and vice versa. To the best of our knowledge, our model produces the most accurate automatic height estimates reported, with the error having a standard deviation of 26.7 mm.
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Bibtex
@inproceedings{gallagher_iccv_09_demographics,
author = {A. Gallagher and A. Blose and T. Chen},
title = {Jointly Estimating Demographics and Height with a Calibrated Camera},
booktitle = {Proc. ICCV},
year = {2009},
}
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