  Recently, we have seen an increase in the global facial recognition market
size. Despite significant advances in face recognition technology with the
adoption of convolutional neural networks, there are still open challenges, as
when there is makeup in the face. To address this challenge, we propose and
evaluate the adoption of facial parts to fuse with current holistic
representations. We propose two strategies of facial parts: one with four
regions (left periocular, right periocular, nose and mouth) and another with
three facial thirds (upper, middle and lower). Experimental results obtained in
four public makeup face datasets and in a challenging cross-dataset protocol
show that the fusion of deep features extracted of facial parts with holistic
representation increases the accuracy of face verification systems and
decreases the error rates, even without any retraining of the CNN models. Our
proposed pipeline achieved state-of-the-art performance for the YMU dataset and
competitive results for other three datasets (EMFD, FAM and M501).
