Abstract
Face data happens everywhere and face matching/verification is the must, such as it helps track criminals; unlock your mobile phone; and pay your bill without credit cards (e.g. Apple Pay). More often, in real world, grayscale image data are used since the color images require more storage. Gray level faces can be studied through two different features: edges and texture since spatial properties could be preserved. Such features could be used to classify faces from one another. Instead of using distance-based feature matching concept, in this paper, a fast machine learning classifier, which we call extreme learning machine (ELM) is used, where we have taken several different activation functions, such as tanh, sigmoid, softlim, hardlim, gaussian, multiquadric and inv_multiquadric. In our tests, five different publicly available datasets, such as Caltech, AR, ColorFERET, IndianFaces and ORL are used. For all activation functions, we have tested with and without feature selection techniques, and compared with the state-of-the-art results.
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
Ahonen, T., Hadid, A., Pietikainen, M.: Face description with local binary patterns: application to face recognition. IEEE Trans. Pattern Anal. Mach. Intell. 28(12), 2037–2041 (2006)
Bosch, A., Zisserman, A., Munoz, X.: Representing shape with a spatial pyramid kernel. In: Proceedings of the 6th ACM International Conference on Image and Video Retrieval, pp. 401–408. ACM (2007)
Bouguelia, M., Nowaczyk, S., Santosh, K.C., Verikas, A.: Agreeing to disagree: active learning with noisy labels without crowdsourcing. Int. J. Mach. Learn. Cybern. 9(8), 1307–1319 (2018)
Cambria, E., et al.: Extreme learning machines. IEEE Intell. Syst. 28(6), 30–59 (2013). https://doi.org/10.1109/MIS.2013.140
Candemir, S., Borovikov, E., Santosh, K., Antani, S., Thoma, G.: Rsilc: rotation-and scale-invariant, line-based color-aware descriptor. Image Vis. Comput. 42, 1–12 (2015)
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893. IEEE (2005)
Deboeverie, F., Veelaert, P., Philips, W.: Face analysis using curve edge maps. In: International Conference on Image Analysis and Processing, pp. 109–118. Springer (2011)
Ding, S., Zhao, H., Zhang, Y., Xu, X., Nie, R.: Extreme learning machine: algorithm, theory and applications. Artif. Intell. Rev. 44(1), 103–115 (2015). https://doi.org/10.1007/s10462-013-9405-z
Gao, Y., Leung, M.K.: Face recognition using line edge map. IEEE Trans. Pattern Anal. Mach. Intell. 24(6), 764–779 (2002)
Grauman, K., Darrell, T.: The pyramid match kernel: discriminative classification with sets of image features. In: Tenth IEEE International Conference on Computer Vision (ICCV’05), vol. 2, pp. 1458–1465. IEEE (2005)
Huang, G., Huang, G.B., Song, S., You, K.: Trends in extreme learning machines. Neural Netw. 61(C), 32–48 (2015). https://doi.org/10.1016/j.neunet.2014.10.001
Huang, G., Wang, D., Lan, Y.: Extreme learning machines: a survey. Int. J. Mach. Learn. Cybern. 2(2), 107–122 (2011). https://doi.org/10.1007/s13042-011-0019-y
Hussain, M.F., Wang, H., Santosh, K.C.: Gray level face recognition using spatial features. In: Santosh, K.C., Hegadi, R.S. (eds.): RTIP2R 2018. CCIS, vol. 1035, pp. 216–229 (2019)
Lazebnik, S., Schmid, C., Ponce, J.: Beyond bags of features: spatial pyramid matching for recognizing natural scene categories. In: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’06), vol. 2, pp. 2169–2178. IEEE (2006)
Oliva, A., Torralba, A.: Modeling the shape of the scene: a holistic representation of the spatial envelope. Int. J. Comput. Vis. 42(3), 145–175 (2001)
Pedregosa, F., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011)
Santosh, K.C., Lamiroy, B., Wendling, L.: Integrating vocabulary clustering with spatial relations for symbol recognition. Int. J. Doc. Anal. Recogn. 17(1), 61–78 (2014)
Turk, M.A., Pentland, A.P.: Face recognition using eigenfaces. In: Proceedings CVPR’91, IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1991, pp. 586–591. IEEE (1991)
Zong, W., Huang, G.: Face recognition based on extreme learning machine. Neurocomputing 74(16), 2541–2551 (2011). https://doi.org/10.1016/j.neucom.2010.12.041
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Wang, H. et al. (2019). An Empirical Study: ELM in Face Matching. In: Santosh, K., Hegadi, R. (eds) Recent Trends in Image Processing and Pattern Recognition. RTIP2R 2018. Communications in Computer and Information Science, vol 1035. Springer, Singapore. https://doi.org/10.1007/978-981-13-9181-1_25
Download citation
DOI: https://doi.org/10.1007/978-981-13-9181-1_25
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-9180-4
Online ISBN: 978-981-13-9181-1
eBook Packages: Computer ScienceComputer Science (R0)