Face Age Estimation and the Other-race Effect

Abstract

Age estimation is an automated method of predicting human age from 2-D facial feature representations. The majority of studies carried out in this research area use the FG-NET and MORPH 2 databases to train and test developed systems, which are lacking in black-face content. Most age frauds are perpetuated in the sub-Saharan African region due to the unavailability of an official database and unregistered births in the rural areas. The issues of unverified age in the region made it possible for under-age voters, under-age drivers, and the engagement of over-aged sportsmen. The other-race effect could reduce the performance of face recognition techniques, which could make techniques that work for white faces underperform when deployed for use in the predominately black face region. This study examines the other-race effect on face-based age estimation by analyzing the accuracy of an age estimation system trained with predominantly black faces against the same age estimation system trained with predominately white faces. The developed age estimation system uses a genetic algorithm-artificial neural network classifier and local binary pattern for texture and shape feature extraction. A total of 170 black faces were used for system testing. The result showed that the age estimation system trained with the predominantly black face database (GA-ANN-AES-855) outperformed the system trained with predominantly white faces (GA-ANN-AES-255) on testing with the aforementioned black face samples. The results obtained from the simulation were further subjected to inferential statistics, which established that the improvement in the correct classification rate was statistically significant. Hence, the other-race effect affects face-based age estimation systems.