An Examination of Bias of Facial Analysis based BMI Prediction Models

TL;DR

This study investigates racial and gender bias in facial analysis-based BMI prediction models, revealing significant disparities in prediction accuracy across different demographic groups and highlighting the importance of bias evaluation.

Contribution

It is the first to evaluate bias in facial analysis-based BMI prediction models across race and gender, identifying disparities and underlying facial feature differences.

Findings

Least prediction error for Black Males

Highest error for White Females

Facial feature changes correlate with BMI increases

Abstract

Obesity is one of the most important public health problems that the world is facing today. A recent trend is in the development of intervention tools that predict BMI using facial images for weight monitoring and management to combat obesity. Most of these studies used BMI annotated facial image datasets that mainly consisted of Caucasian subjects. Research on bias evaluation of face-based gender-, age-classification, and face recognition systems suggest that these technologies perform poorly for women, dark-skinned people, and older adults. The bias of facial analysis-based BMI prediction tools has not been studied until now. This paper evaluates the bias of facial-analysis-based BMI prediction models across Caucasian and African-American Males and Females. Experimental investigations on the gender, race, and BMI balanced version of the modified MORPH-II dataset suggested that the error rate in BMI prediction was least for Black Males and highest for White Females. Further, the psychology-related facial features correlated with weight suggested that as the BMI increases, the changes in the facial region are more prominent for Black Males and the least for White Females. This is the reason for the least error rate of the facial analysis-based BMI prediction tool for Black Males and highest for White Females.