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Sensing and Imaging

, 19:5 | Cite as

Unsupervised Feature Learning with Single Layer ICANet for Face Recognition

  • Tianyu Geng
  • Yongqing Zhang
  • Ying Cai
  • Menglong Yang
  • Hailong Jing
Original Paper
  • 129 Downloads
Part of the following topical collections:
  1. Recent Developments in Sensing and Imaging

Abstract

Compared to supervised learning, unsupervised learning allows systems to learn consistent patterns from cheap and abundant unlabeled data, without the need for manual annotation. In this paper, we present a novel unsupervised feature learning method with single layer (SL) network based independent component analysis (ICA) filters called SL-ICANet, with the goal of achieving compact and robust facial feature representation. Our contributions are twofold: (i) We developed a single-layer convolutional network for unsupervised learning wherein the trainable kernels are replaced by ICA filters. (ii) We extended our SL-ICANet to use multi-scale information for better feature learning. Extensive experiments on two popular face recognition benchmarks, namely, labeled faces in the wild and facial recognition technology show that the proposed method might serve as a simple but highly competitive baseline for face recognition.

Keywords

Unsupervised learning Face recognition LFW FERET ICANet 

Notes

Acknowledgements

This work is supported by National Natural Science Foundation of China (Grant Nos. 61402307, 61702058) National Key Scientific Instrument and Equipment Development Project of China (No. 2013YQ49087903), the Scientific Research Foundation of CUIT (Nos. KYTZ201717, J201706) and the China Postdoctoral Science and Foundation No. 2017M612948.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Tianyu Geng
    • 1
    • 2
  • Yongqing Zhang
    • 3
    • 4
  • Ying Cai
    • 5
  • Menglong Yang
    • 6
  • Hailong Jing
    • 2
  1. 1.College of Computer ScienceSichuan UniversityChengduPeople’s Republic of China
  2. 2.National Key Laboratory of Fundamental Science on Synthetic VisionChengduPeople’s Republic of China
  3. 3.School of Computer ScienceChengdu University of Information TechnologyChengduPeople’s Republic of China
  4. 4.School of Computer Science and EngineeringUniversity of Electronic Science and Technology of ChinaChengduPeople’s Republic of China
  5. 5.College of Information EngineeringSichuan Agricultural UniversityYaanPeople’s Republic of China
  6. 6.School of Aeronautics and AstronauticsSichuan UniversityChengduPeople’s Republic of China

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