Predicting gender and weight from human metrology using a copula model

We investigate the use of human metrology for the prediction of certain soft biometrics, viz. gender and weight. In particular, we consider geometric measurements from the head, and those from the remaining parts of the human body, and analyze their potential in predicting gender and weight. For gender prediction, the proposed model results in a 0.7% misclassification rate using both body and head information, 1.0% using only body information, and 12.2% using only head information on the CAESAR 1D database consisting of 2,369 subjects. For weight prediction, the proposed model gives 0.01 mean absolute error (in the range 0 to 1) using both body and head information, 0.01 using only body information, and 0.07 using only measurements from the head. This leads to the observation that human body metrology contains enough information for reliable prediction of gender and weight. Furthermore, we investigate the efficacy of the model in practical applications, where metrology data may be missing or severely contaminated by various sources of noises. The proposed copula-based technique is observed to reduce the impact of noise on prediction performance.