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Multiplex image representation for enhanced recognition

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Abstract

A multiscale approach to exploiting existing image descriptors (LBP and HOG) is proposed recently in order to enhance face recognition performance (Ubiquitous computing and ambient intelligence. Personalisation and user adapted services. Springer, 532–539, 2014) and (A multiscale method for HOG-based face and palmprint recognition. Technical report, Ulster University, 2015), where multiple single-sourced, spatially-varied feature vectors at different scales are calculated from images and then fused through an image distance function. This multiscale approach has led to significant improvements in face recognition over the single scale approach. In this paper we present an analysis of this multiscale approach from feature engineering perspective and evaluation result for the image distance function on palmprint recognition, thus providing an insight into and also extending the applicability of this approach. We also present a new method of utilising these spatially-varied feature vectors from an image—joining these feature vectors head to tail to form a larger feature vector which is used as a multiplex representation of the image. Such an image representation can then be used by any vector-based feature extraction and classification algorithms. This representation scheme is evaluated experimentally in face recognition, and the results show this scheme is competitive to the distance-based method having the additional advantage of being usable in a wider range of machine learning algorithms. The main contributions of this paper are (1) an insight into this multiscale approach to utilising existing image descriptors such as LBP and HOG; (2) a new method of using these multiple feature vectors; and (3) extension of the multiscale approach to palmprint recognition.

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Acknowledgments

This work is partially supported by Hu Guozan Study-Abroad Grant for Graduates of Fujian Normal University.

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Authors and Affiliations

  1. Key Lab of Network Security and Cryptology, School of Mathematics and Computer Science, Fujian Normal University, Fuzhou, People’s Republic of China

    Xin Wei, Gongde Guo & Huan Wan

  2. School of Computing and Mathematics, University of Ulster at Jordanstown, Belfast, UK

    Hui Wang

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  1. Xin Wei
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  2. Hui Wang
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  3. Gongde Guo
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  4. Huan Wan
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Correspondence to Xin Wei.

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Wei, X., Wang, H., Guo, G. et al. Multiplex image representation for enhanced recognition. Int. J. Mach. Learn. & Cyber. 9, 383–392 (2018). https://doi.org/10.1007/s13042-015-0427-5

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