2021/01/18 by Marcus de Assis Angeloni, Angeloni, Marcus de Assis, Hélio Pedrini +1
Computer Science · Medicine · #Biometric Identification and Security #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Face recognition and analysis #Facial Nerve Paralysis Treatment and Research
paper · pdf · doi:10.48550/arxiv.2101.07338
openalex publication_date 2021/01/18 · openalex created_date 2022/10/06 · openalex updated_date 2026/07/28
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).