Abstract
This paper proposes a multi-modal fusion method that based on quaternion, and principal component analysis (PCA) in quaternion field is involved in our algorithm. We can fuse four different features into quaternion and complete the recognition process in quaternion field. This algorithm reduces the equal error rate (EER) while fusing more kinds of features. Our experiments that fuses three kinds of modalities and four different features with two kinds of modalities respectively show a observably improvement on recognition rate with the proposed algorithm.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Jain, A.K.: Biometric Recognition: Q&A. Nature, 449 (2007)
Bigün, E., Bigün, J., Duc, B., et al.: Expert conciliation for multimodal person authentication systems using bayesian statistics. In: Bigün, J., Borgefors, G., Chollet, G. (eds.) AVBPA 1997. LNCS, vol. 1206, pp. 291–300. Springer, Heidelberg (1997)
Duc, B., Bigün, E.S., Bigün, J., et al.: Fusion of Audio and Video Information for Multimodal Person Authentication. Pattern Recognition Letters 18(9), 835–843 (1997)
Ling Lin, X., Zhou, M., et al.: Research on multimodal biometric authentication using feature level fusion. Computer Engineering and Design 35(8), 2849–2852 (2011)
Yuan, L., Mu, Z.C., Zeng, H.: Multimodal recognition using face and ear. Journal of University of Science and Technology Beijing 29(2), 191–193 (2007)
Ma, H.: Research of recognition method based on the feature layer fusion of palmprint and hand vein. Tianjin University of Technology (2014)
Rattani, A., Kisku, D.R., Bicego, M., et al.: Robust Feature-level Multibiometric Classification. In: Proceedings of Biometric Consortium Conference, pp. 1–6. IEEE, Baltimore, (2006)
Yao, Y., Jing, X., Wong, H.: Face and Palmprint Feature Level Fusion for Single Sample Biometrics Recognition. Neurocomputing Letters 70(7–9), 1582–1586 (2007)
Jolliffe, I.T.: Principal Component Analysis. Springer Verlag, New York (1986)
Scholkopf, B., Smola, A.J., Muller, K.R.: Nonlinear Component Analysis as a Kernel Eigenvalue Problem. Nerual Computation 10(5), 1299–1319 (1998)
Lang, F.N., et al.: Obtain Method of Quaternion Matrix Orthogonal Eigenvector Set and Its Application in Color Face Recognition. Acta Automatica Sinica 34(2), 121–129 (2008)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Chen, M., Wang, C., Meng, X., Wang, Z. (2016). Quaternion Principal Component Analysis for Multi-modal Fusion. In: Zin, T., Lin, JW., Pan, JS., Tin, P., Yokota, M. (eds) Genetic and Evolutionary Computing. GEC 2015. Advances in Intelligent Systems and Computing, vol 388. Springer, Cham. https://doi.org/10.1007/978-3-319-23207-2_2
Download citation
DOI: https://doi.org/10.1007/978-3-319-23207-2_2
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-23206-5
Online ISBN: 978-3-319-23207-2
eBook Packages: EngineeringEngineering (R0)