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).
翻译:最近,我们看到了全球面部识别市场规模的扩大。尽管在通过进化神经网络后,面部识别技术取得了显著进步,但仍然存在一些公开的挑战,如在面部化妆时。为了应对这一挑战,我们提议并评估采用面部部分与当前整体表现相结合的情况。我们提出了两个面部部分的战略:一个是四个区域(左透镜、右透镜、鼻和口),另一个是面部3/3(上、中、下)。在四个公共面部数据集和具有挑战性的交叉数据集协议中取得的实验结果显示,从面部部分提取的深层特征与整体代表性相结合,提高了面部核查系统的准确性,降低了误差率,即使没有对CNN模型进行任何再培训。我们提议的管道实现了YMU数据集的最新性能,其他3个数据集(EMFD、FAM和M501)的竞争结果。