论文标题
FusiformNet：提取不同级别的区分面特征
FusiformNet: Extracting Discriminative Facial Features on Different Levels
论文作者
论文摘要
在过去的几年中，基于深层神经网络的面部识别的研究已经随着任务特定的损失功能，图像归一化和增强，网络体系结构等方法的发展而发展。但是，很少有方法可以关注人的面孔在人之间的差异。我提出了fusiformnet，这是一个普遍和本地的人际关系差异，这是一种用于提取特征提取的新型框架，它利用了判别性面部特征的性质。在野生基准测试的标记面的图像无限制设置上测试，该方法达到了96.67％的最新精度，没有标记的外部数据，图像增强，标准化或特殊损失函数。同样，该方法在Casia-Webface数据集中进行了预训练时，在先前的最先进的方面也可以执行该方法。考虑到其提取一般和局部面部特征的能力，FusiformNet的效用可能不仅限于面部识别，还可以扩展到其他基于DNN的任务。
Over the last several years, research on facial recognition based on Deep Neural Network has evolved with approaches like task-specific loss functions, image normalization and augmentation, network architectures, etc. However, there have been few approaches with attention to how human faces differ from person to person. Premising that inter-personal differences are found both generally and locally on the human face, I propose FusiformNet, a novel framework for feature extraction that leverages the nature of discriminative facial features. Tested on Image-Unrestricted setting of Labeled Faces in the Wild benchmark, this method achieved a state-of-the-art accuracy of 96.67% without labeled outside data, image augmentation, normalization, or special loss functions. Likewise, the method also performed on a par with previous state-of-the-arts when pre-trained on CASIA-WebFace dataset. Considering its ability to extract both general and local facial features, the utility of FusiformNet may not be limited to facial recognition but also extend to other DNN-based tasks.
