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    A multimodal deep learning framework using local feature representations for face recognition

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    Publication date
    2017
    Author
    Al-Waisy, Alaa S.
    Qahwaji, Rami S.R.
    Ipson, Stanley S.
    Al-Fahdawi, Shumoos
    Keyword
    Face recognition; Curvelet transform; Fractal dimension; Fractional Brownian motion; Deep belief network; SDUMLA-HMT database; FERET database; LFW database
    Rights
    © 2017 The Authors. This article is distributed under the terms of the Creative Commons CC-BY License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
    Peer-Reviewed
    Yes
    
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    Abstract
    The most recent face recognition systems are mainly dependent on feature representations obtained using either local handcrafted-descriptors, such as local binary patterns (LBP), or use a deep learning approach, such as deep belief network (DBN). However, the former usually suffers from the wide variations in face images, while the latter usually discards the local facial features, which are proven to be important for face recognition. In this paper, a novel framework based on merging the advantages of the local handcrafted feature descriptors with the DBN is proposed to address the face recognition problem in unconstrained conditions. Firstly, a novel multimodal local feature extraction approach based on merging the advantages of the Curvelet transform with Fractal dimension is proposed and termed the Curvelet–Fractal approach. The main motivation of this approach is that theCurvelet transform, a newanisotropic and multidirectional transform, can efficiently represent themain structure of the face (e.g., edges and curves), while the Fractal dimension is one of the most powerful texture descriptors for face images. Secondly, a novel framework is proposed, termed the multimodal deep face recognition (MDFR)framework, to add feature representations by training aDBNon top of the local feature representations instead of the pixel intensity representations. We demonstrate that representations acquired by the proposed MDFR framework are complementary to those acquired by the Curvelet–Fractal approach. Finally, the performance of the proposed approaches has been evaluated by conducting a number of extensive experiments on four large-scale face datasets: the SDUMLA-HMT, FERET, CAS-PEAL-R1, and LFW databases. The results obtained from the proposed approaches outperform other state-of-the-art of approaches (e.g., LBP, DBN, WPCA) by achieving new state-of-the-art results on all the employed datasets.
    URI
    http://hdl.handle.net/10454/13122
    Version
    Published version
    Citation
    Al-Waisy AS, Qahwaji R, Ipson S et al (2017) A multimodal deep learning framework using local feature representations for face recognition. Machine Vision and Applications. 29(1): 35-54.
    Link to publisher’s version
    https://doi.org/10.1007/s00138-017-0870-2
    Type
    Article
    Collections
    Engineering and Informatics Publications

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