Volume 29 Issue 6
Dec.  2020
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WANG Zhifang, ZHEN Jiaqi, ZHU Fuzhen, HAN Qi. Quaternion Kernel Fisher Discriminant Analysis for Feature-Level Multimodal Biometric Recognition[J]. Chinese Journal of Electronics, 2020, 29(6): 1085-1092. doi: 10.1049/cje.2020.09.009
Citation: WANG Zhifang, ZHEN Jiaqi, ZHU Fuzhen, HAN Qi. Quaternion Kernel Fisher Discriminant Analysis for Feature-Level Multimodal Biometric Recognition[J]. Chinese Journal of Electronics, 2020, 29(6): 1085-1092. doi: 10.1049/cje.2020.09.009

Quaternion Kernel Fisher Discriminant Analysis for Feature-Level Multimodal Biometric Recognition

doi: 10.1049/cje.2020.09.009
  • Received Date: 2019-10-31
  • Publish Date: 2020-12-25
  • Quaternion kernel Fisher discriminant analysis (QKFDA) is proposed for feature level multimodal biometric recognition. In quaternion division ring, QKFDA extracts the most discriminative information from the quaternion fusion feature sets by maximizing the betweenclass variance while minimizing the within-class variance. A complete two-phases framework of QKFDA is developed: Quaternion kernel principal component analysis (QKPCA) plus Quaternion linear discriminant analysis(QLDA). Two experiments are designed: experiment I fuses four different features of face and plamprint, experiment II fuses three different features of face, plamprint and signature. The experimental results show that QKFDA is superior to both traditional feature fusion methods (series rule and weighted sum rule)and other quaternion feature fusion methods (QPCA, QFDA, QLPP and QKPCA).
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  • Z. Hu and J. Chen, "Feature extraction model based on multi-layered deep local subspace sparse optimization", Acta Electronica Sinica, Vol.45, No.10, pp.2383-2389, 2017. (in Chinese)
    D. Zhou, J. Zhang and S. Zhu, "Multi-directional and multiscale Gabor feature representation and its matching algorithm", Acta Electronica Sinica, Vol.47, No.9, pp.1998-2002, 2019. (in Chinese)
    J. Yang, J. Yang and A. Frangib, "Combined Fisherfaces framework", Image Vision Computing, Vol.21, pp.1037-1044, 2003.
    Z. Wang, Q. Han and X. Niu, "Complex kernel PCA for multimodal biometric recognition", IEICE Electronics Express, Vol.6, No.16, pp.1131-1136, 2009.
    Z. Wang, Q. Li, X. Niu, et al., "Multimodal biometric recognition based on complex KFDA", Proc. of the Fifth International Conference on Information Assurance and Security, Xi'an, China, pp.18-20, 2009.
    R. Lan and Y. Zhou, "Quaternion-Michelson descriptor for color image classification", IEEE Transactions on Image Processing, Vol.25, No.11, pp.5281-5292, 2016.
    Q. Yin, J. Wang, X. Luo, et al., "Quaternion convolutional neural network for color image classification and forensics", IEEE Access, Vol.7, pp.20293-20301, 2019.
    B. Chen, J. Yang, B. Jeon, et al., "Kernel quaternion principal component analysis and its application in RGB-D object recognition", Neurocomputing, Vol.266, pp.293-303, 2017.
    B. Chen, Y. Gao, J. Yang, et al., "An RGB-D target recognition method based on quaternion generalized discriminant analysis", Patent, 201811176644.0, China, 2019-03-08(in Chinese).
    B. Chen, M. Yu and Q. Su, "Fractional quaternion zernike moments for robust color image copy-move forgery detection", IEEE Access, Vol.6, pp.56637-56646, 2018.
    F. Lang, J. Zhou, B. Yan, et al., "Obtain method of quaternion matrix orthogonal eigenvector set and its application in color face recognition", Acta Automatica Sinica, Vol.34, No.2, pp.121-129, 2008. (in Chinese)
    F. Lang, J. Zhou, F. Zhong, et al., "Quaternion based image information parallel fusion", Acta Automatica Sinica, Vol.33, No.11, pp.1136-1143, 2007. (in Chinese)
    Z. Wang, Z. Zhen, Y. Li, et al., "Multi-feature multimodal biometric recognition based on quaternion locality preserving projection", Chinese Journal of Electronics, Vol.28, No.4, pp.789-796, 2019.
    W. Li, "quaternion matrix", National University of Defense Technology Press, Changsha, China, pp.99-103, 2002(in Chinese).
    J. Yang, A. Frangib, J. Yang, et al.. "KPCA plus LDA:A complete kernel fisher discriminant framework for feature extraction and recognition", IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.27, No.2, pp.230-244, 2005.
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