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Unsupervised Feature Learning with Single Layer ICANet for Face Recognition

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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.

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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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Authors and Affiliations

  1. College of Computer Science, Sichuan University, Chengdu, People’s Republic of China

    Tianyu Geng

  2. National Key Laboratory of Fundamental Science on Synthetic Vision, Chengdu, People’s Republic of China

    Tianyu Geng & Hailong Jing

  3. School of Computer Science, Chengdu University of Information Technology, Chengdu, People’s Republic of China

    Yongqing Zhang

  4. School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, People’s Republic of China

    Yongqing Zhang

  5. College of Information Engineering, Sichuan Agricultural University, Yaan, 625014, People’s Republic of China

    Ying Cai

  6. School of Aeronautics and Astronautics, Sichuan University, Chengdu, People’s Republic of China

    Menglong Yang

Authors
  1. Tianyu Geng
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  2. Yongqing Zhang
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  3. Ying Cai
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  4. Menglong Yang
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  5. Hailong Jing
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Correspondence to Yongqing Zhang.

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This article is part of the Topical Collection on Recent Developments in Sensing and Imaging.

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Geng, T., Zhang, Y., Cai, Y. et al. Unsupervised Feature Learning with Single Layer ICANet for Face Recognition. Sens Imaging 19, 5 (2018). https://doi.org/10.1007/s11220-018-0188-9

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  • DOI: https://doi.org/10.1007/s11220-018-0188-9

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