Colour face recognition using fuzzy quaternion-based discriminant analysis

  • Shuzhe Bao
  • Xiaoning SongEmail author
  • Guosheng Hu
  • Xibei Yang
  • Chunli Wang
Original Article


Colour information has been shown to be effective in improving object recognition performance. In this paper, we propose a novel quaternion-based colour model with enhanced fuzzy parameterized discriminant analysis to perform face recognition. The proposed method represents and classifies colour images by using an improved fuzzy quaternion-based discriminant (FQD) model, which is effective for colour image feature representation, extraction and classification. More specifically, each pixel in a colour image is first assigned a quaternion number, and a quaternion-based vector is then generated to represent this colour image. Second, an enhanced fuzzy parameterized discriminant analysis is used to transform the original quaternion-based vector into an optimized discriminant quaternion space. Third, colour face recognition is conducted by interpreting the colour feature model as fuzzy weight measurement in a quaternion discriminant analysis. The main contribution of this paper is that it provides a novel fuzzy supervised learning approach to reconstruct the quaternion-based discriminant vector space, thus showing the importance of the FQD characteristic from colour spaces for colour-image-based face recognition. Experimental results on the AR and Georgia Tech colour datasets demonstrate the effectiveness of the proposed method.


Quaternion-based vector Fuzzy parameterized discriminant analysis Colour image recognition 



We would like to thank the anonymous reviewers for their constructive suggestions. This work was funded by the National Key Research and Development Program of China (2016YFD0401204), the National Science and Technology Support Program of China (No. 2015 BAD17B02), the Natural Science Foundation of Jiangsu Province (Grants no. BK20161135), China Postdoctoral Science Foundation (Grant no. 2016M590407), the Fundamental Research Funds for the Central Universities (Grant no. JUSRP115A29, JUSRP51618B), the Open Project Program of the Key Laboratory of Intelligent Perception, Systems for High-Dimensional Information of the Ministry of Education (No. JYB201603) and the Open Project Program of the Fujian Provincial Key Laboratory of Information Processing and Intelligent Control (Minjiang University, No. MJUKF201709).


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Copyright information

© Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  • Shuzhe Bao
    • 1
    • 2
  • Xiaoning Song
    • 3
    • 4
    • 5
    Email author
  • Guosheng Hu
    • 6
  • Xibei Yang
    • 7
  • Chunli Wang
    • 1
  1. 1.School of Information Science and TechnologyDalian Maritime University, DLMUDalianChina
  2. 2.School of Computer Science and TechnologyDalian Nationalities University, DLNUDalianChina
  3. 3.School of Internet of Things EngineeringJiangnan UniversityWuxiChina
  4. 4.School of Computer and SoftwareNanjing University of Information Science and TechnologyNanjingChina
  5. 5.Fujian Provincial Key Laboratory of Information Processing and Intelligent ControlMinjiang UniversityFuzhouChina
  6. 6.Centre for Vision, Speech and Signal ProcessingUniversity of SurreyGuildfordUK
  7. 7.Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of EducationNanjing University of Science and TechnologyNanjingChina

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