  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.
