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Over the last several years, research on facial recognition based on DeepNeural 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 fromperson to person. Premising that inter-personal differences are found bothgenerally and locally on the human face, I propose FusiformNet, a novelframework for feature extraction that leverages the nature of discriminativefacial features. Tested on Image-Unrestricted setting of Labeled Faces in theWild benchmark, this method achieved a state-of-the-art accuracy of 96.67%without labeled outside data, image augmentation, normalization, or specialloss functions. Likewise, the method also performed on a par with previousstate-of-the-arts when pre-trained on CASIA-WebFace dataset. Considering itsability to extract both general and local facial features, the utility ofFusiformNet may not be limited to facial recognition but also extend to otherDNN-based tasks.

FusiformNet: Extracting Discriminative Facial Features on Different Levels