Discriminative Representation Learning for Face Recognition

  • Chia-Hao Tang
  • Gee-Sern Jison HsuEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1153)


A significant progress has been made on face recognition in recent years because of the rapid development of deep learning approaches. Deep learning approaches offer a powerful toolbox for tackling many aspects of face recognition, including the search for effective discriminative features. We compare several state-of-the-art face recognition methods and combine different modules from those methods to propose a special approach for discriminative representation learning. Our approach has a special architecture for representation learning combined with a latest design of classification loss function, making it a highly effective solution for uncontrolled face recognition. Experimented on the Labeled Faces in the Wild (LFW), the Celebrities in Frontal-Profile dataset (CFP), and the AgeDB datasets, our approach shows competitive performance to other state-of-the-art methods.


Face recognition Deep learning Representation learning 


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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.National Taiwan University of Science and TechnologyTaipeiTaiwan

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