  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.
