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Race classification is a long-standing challenge in the field of face imageanalysis. The investigation of salient facial features is an important task toavoid processing all face parts. Face segmentation strongly benefits severalface analysis tasks, including ethnicity and race classification. We propose araceclassification algorithm using a prior face segmentation framework. A deepconvolutional neural network (DCNN) was used to construct a face segmentationmodel. For training the DCNN, we label face images according to seven differentclasses, that is, nose, skin, hair, eyes, brows, back, and mouth. The DCNNmodel developed in the first phase was used to create segmentation results. Theprobabilistic classification method is used, and probability maps (PMs) arecreated for each semantic class. We investigated five salient facial featuresfrom among seven that help in race classification. Features are extracted fromthe PMs of five classes, and a new model is trained based on the DCNN. Weassessed the performance of the proposed race classification method on fourstandard face datasets, reporting superior results compared with previousstudies.

A Facial Feature Discovery Framework for Race Classification Using Deep Learning