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    Stacked Face De-Noising Auto Encoders for Expression-Robust Face Recognition

    Access Status
    Fulltext not available
    Authors
    Pathirage, C.
    Li, L.
    Liu, Wan-Quan
    Zhang, M.
    Date
    2016
    Type
    Conference Paper
    
    Metadata
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    Citation
    Pathirage, C. and Li, L. and Liu, W. and Zhang, M. 2016. Stacked Face De-Noising Auto Encoders for Expression-Robust Face Recognition, in Proceedings of the 2015 International Conference on Digital Image Computing: Techniques and Applications (DICTA), Nov 23-25 2015. Adelaide, SA: IEEE.
    Source Title
    2015 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2015
    DOI
    10.1109/DICTA.2015.7371310
    ISBN
    9781467367950
    School
    Department of Computing
    URI
    http://hdl.handle.net/20.500.11937/38384
    Collection
    • Curtin Research Publications
    Abstract

    Recent advancement in unsupervised and transfer learning methods of deep learning networks has seen a complete paradigm shift in machine learning. Inspired by the recent evolution of deep learning (DL) networks that demonstrates a proven pathway of addressing challenging dilemmas in various problem domains, we propose a novel DL framework for expression-robust feature acquisition. The framework exploits the contributions of different colour components in different local face regions by recovering the neutral expression from various expressions. Furthermore, the framework rigorously de-noises a face with dynamic expressions in a progressive way thus it is termed as stacked face de-noising auto-encoders (SFDAE). The high-level expression-robust representations that are learnt via this framework will not only yield better reconstruction of neutral expression faces but also boost the performance of the subsequent LDA[1] classifier. The experimental results reveal the superiority of the proposed method to the existing works in terms of its generalization ability and the high recognition accuracy.

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