Abstract
In recent years, non-linear dimensionality reduction methods are getting popular for the handling image data due to non-linearity present in data. For the image recognition task, non-linear dimensionality reduction methods are not useful as it is unable to find the out-of-sample data representation in the reduced subspace. To handle non-linearity of the data, the kernel method is used, which find the feature space from higher dimensional space. One can find the reduce subspace representation by applying the linear dimensionality reduction techniques in the feature space. Extended Locality Preserving Projection (ELPP) tries to capture non-linearity by maintaining neighborhood information in the reduce subspace but fails to capture complex-nonlinear changes. So kernel variants of ELPP are proposed to handle non-linearity present in the data. This article addressed kernel variants of the ELPP which explored the complex non-linear changes of the facial expression recognition. The proposed kernel variants of the ELPP is applied for face recognition on some benchmark databases.
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
Similar content being viewed by others
References
Turk, M., Pentland, A.: Eigenfaces for recognition. J. Cogn. Neurosci. 3(1), 71–86 (1991)
Belhumeur, P.N., Hespanha, J.P., Kriegman, D.J.: Eigenfaces vs. fisherfaces: recognition using class specific linear projection. IEEE Trans. Pattern Anal. Mach. Intell. 7, 711–720 (1997)
Hyvarinen, A.: Survey on independent component analysis. Neural Comput. Surv. 2(4), 94–128 (1999)
He, X., Niyogi, P.: Locality preserving projections. In: Advances in Neural Information Processing Systems, pp. 153–160 (2004)
He, X., Cai, D., Yan, S., Zhang, H.-J.: Neighborhood preserving embedding. In: Tenth IEEE International Conference on Computer Vision (ICCV’05) vol. 1, vol. 2, pp. 1208–1213. IEEE (2005)
Shikkenawis, G., Mitra, S.K.: Locality preserving discriminant projection. In: IEEE International Conference on Identity, Security and Behavior Analysis (ISBA 2015), pp. 1–6. IEEE (2015)
Shikkenawis, G., Mitra, S.K.: On some variants of locality preserving projection. Neurocomputing 173, 196–211 (2016)
Schölkopf, B., Smola, A., Müller, K.-R.: Kernel principal component analysis. In: Gerstner, W., Germond, A., Hasler, M., Nicoud, J.-D. (eds.) ICANN 1997. LNCS, vol. 1327, pp. 583–588. Springer, Heidelberg (1997). https://doi.org/10.1007/BFb0020217
Liwicki, S., Tzimiropoulos, G., Zafeiriou, S., Pantic, M.: Euler principal component analysis. Int. J. Comput. Vision 101(3), 498–518 (2013)
Tzimiropoulos, G., Zafeiriou, S., Pantic, M.: Subspace learning from image gradient orientations. IEEE Trans. Pattern Anal. Mach. Intell. 34(12), 2454–2466 (2012)
Kanopoulos, N., Vasanthavada, N., Baker, R.L.: Design of an image edge detection filter using the sobel operator. IEEE J. Solid-State Circuits 23(2), 358–367 (1988)
Lyons, M.J., Akamatsu, S., Kamachi, M., Gyoba, J., Budynek, J.: The Japanese female facial expression (JAFFE) database. In: Proceedings of Third International Conference on Automatic Face and Gesture Recognition, pp.14–16 (1998)
Kanade, T., Cohn, J.F., Tian, Y.: Comprehensive database for facial expression analysis. In: Proceedings Fourth IEEE International Conference on Automatic Face and Gesture Recognition (Cat. No. PR00580), pp. 46–53. IEEE (2000)
Yu-Feng, Y., Dai, D.-Q., Ren, C.-X., Huang, K.-K.: Discriminative multi-layer illumination-robust feature extraction for face recognition. Pattern Recogn. 67, 201–212 (2017)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Bhatt, P., Sujata, Mitra, S.K. (2020). Kernel Variants of Extended Locality Preserving Projection. In: Nain, N., Vipparthi, S., Raman, B. (eds) Computer Vision and Image Processing. CVIP 2019. Communications in Computer and Information Science, vol 1148. Springer, Singapore. https://doi.org/10.1007/978-981-15-4018-9_12
Download citation
DOI: https://doi.org/10.1007/978-981-15-4018-9_12
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-4017-2
Online ISBN: 978-981-15-4018-9
eBook Packages: Computer ScienceComputer Science (R0)