Abstract
Based on the study of image gradient orientation and relevant technique about customers, this paper has proposed a algorithm related with customers based on image gradient orientation (CS-IGO-LDA). Face images are vulnerable to illumination changes, resulting in most of the traditional subspace learning algorithms which rely on image representation information are robust. In order to alleviate this problem, we represent the original samples by using image gradient orientation rather than the pixel intensity. And, in order to better describe the differences between different categories, we use methods related with customers to extract sample feature vector of each individual. The proposed CS-IGO-LDA method has made full use of the advantage of image gradient orientation and methods related with customers in face recognition. Experiments in face databases of Yale, JAFFE and XM2VTS have proved the validity of the new algorithm in face recognition and face verification.
Fund Project: general project fund from education department of Zhejiang province (Y201738405).
Authors’ Information:
X. Li (1981)—female, lecturer, majoring in and researching art design.
D. Zhang (1962)—male, professor, machine learning is his main researching area.
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
References
Tzimiropoulos, G., Zafeiriou, S.: PAntic., M.: Subspace learning from image gradient orientations. IEEE Trans. Pattern Anal. Mach. Intell. 34(2), 2454–2466 (2012)
Kittler, J.: Face authentication using client specific fisherfaces. In: Proceedings of Center for Vision Speech and Signal Processing. University of Surrey (2001)
Wu, X., Josef, K., Yang, J., Kieron, M., Wang, S., Lu, J.: On dimensionality reduction for client specific discriminant analysis with application to face verification. In: Li, S.Z., Lai, J., Tan, T., Feng, G., Wang, Y. (eds.) SINOBIOMETRICS 2004. LNCS, vol. 3338, pp. 305–312. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-30548-4_35
Su, X.: Face Recognition Algorithms and its Application. Chinese Thesis, Jiangnan University, Wuxi, China (2013)
Yin, H.-F., Wu, X.-J., Sun, X.-Q.: Client specific image gradient orientation for unimodal and multimodal face representation. In: Schwenker, F., Scherer, S., Morency, L.-P. (eds.) MPRSS 2014. LNCS (LNAI), vol. 8869, pp. 15–25. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-14899-1_2
Sun, X.Q., Wu, X.J., Sun, J., Montesinos, P.: Hybrid client specific discriminant analysis and its application to face verification. In: Hatzilygeroudis, I., Palade, V. (eds.) Combinations of Intelligent Methods and Applications. Smart Innovation, Systems and Technologies, vol. 23. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-36651-2_8
Jian, C., Chen, X.: Unsupervised feature Selection based on locality preserving projection and sparse representation. Pattern Recogn. Artif. Intell. 28(3), 247–252 (2015)
Yao, L., Deng, K., Xu, Y.: Face recognition based on gradient information. Comput. Eng. Appl. 46(35), 170–172 (2010)
Tzimiropoulos, G., Zafeiriou, S., Pantic, M.: Principal component analysis of image gradient orientations for face recognition. In: Proceedings of International Conference on Automatic Face & Gesture Recognition and Workshops, pp. 553–558 (2011)
Chen, X., Yang, J., Zhang, D., Liang, J.: Complete large margin linear discriminant analysis using mathematical programming approach. Pattern Recogn. 46(6), 1579–1594 (2013)
Yao, C., Lu, Z., Li, J., Xu, Y., Han, J.: A subset method for improving linear discriminant analysis. Neurocomputing 138, 310–315 (2014)
Acknowledgements
This work was supported in part by the Humanities and Social Science Foundation of Ministry of Education of China under Grant No. 16YJCZH112, by Zhejiang Provincial Natural Science Foundation of China under Grant No. LY16F030012, LY14F020009, and LY16F030016, and by Scientific Research Fund of Zhejiang Provincial Education Department with Grant No. Y201534788.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer-Verlag GmbH Germany
About this chapter
Cite this chapter
Li, X., Zhang, D. (2018). A Novel Algorithm Related with Customers Based on Image Gradient Orientation. In: Pan, Z., Cheok, A., Müller, W. (eds) Transactions on Edutainment XIV. Lecture Notes in Computer Science(), vol 10790. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-56689-3_6
Download citation
DOI: https://doi.org/10.1007/978-3-662-56689-3_6
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-56688-6
Online ISBN: 978-3-662-56689-3
eBook Packages: Computer ScienceComputer Science (R0)