  Race classification is a long-standing challenge in the field of face image
analysis. The investigation of salient facial features is an important task to
avoid processing all face parts. Face segmentation strongly benefits several
face analysis tasks, including ethnicity and race classification. We propose a
raceclassification algorithm using a prior face segmentation framework. A deep
convolutional neural network (DCNN) was used to construct a face segmentation
model. For training the DCNN, we label face images according to seven different
classes, that is, nose, skin, hair, eyes, brows, back, and mouth. The DCNN
model developed in the first phase was used to create segmentation results. The
probabilistic classification method is used, and probability maps (PMs) are
created for each semantic class. We investigated five salient facial features
from among seven that help in race classification. Features are extracted from
the PMs of five classes, and a new model is trained based on the DCNN. We
assessed the performance of the proposed race classification method on four
standard face datasets, reporting superior results compared with previous
studies.
