Improving Makeup Face Verification by Exploring Part-Based Representations
This work addresses a specific problem in face verification for applications where makeup causes performance drops, but it is incremental as it builds on existing methods.
The paper tackled the challenge of face verification with makeup by fusing part-based facial representations with holistic ones, achieving competitive results across four public datasets and reducing error rates without retraining CNN models.
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, such 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 competitive results for the four datasets (EMFD, FAM, M501 and YMU).