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An Improved Scheme of Local Directional Pattern for Texture Analysis with an Application to Facial Expressions

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Computer Analysis of Images and Patterns (CAIP 2017)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10425))

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Abstract

In this paper, several extensions and modifications of Local Directional Pattern (LDP) are proposed with an objective to increase its robustness and discriminative power. Typically, Local Directional pattern generates a code based on the edge response value for the eight directions around a particular pixel. This method ignores the center value which can include important information. LDP uses absolute value and ignores sign of the response which carries information about image gradient and may contain more discriminative information. The sign of the original value carries information about the different trends (positive or negative) of the gradient and may contain some more data. Centered Local Directional Pattern (CLDP), Signed Local Directional Pattern (SLDP) and Centered-SLDP (CSLDP) are proposed in different conditions. Experimental results on 20 texture types using 5 different classifiers in different conditions shows that CLDP in both upper and lower traversal and CSLDP substantially outperforms the formal LDP. All the proposed methods were applied to facial expression emotion application. Experimental results show that SLDP and CLDP outperform original LDP in facial expression analysis.

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References

  1. Eleyan, A., Demirel, H.: Co-occurrence matrix and its statistical features as a new approach for face recognition. Turkish J. Electr. Eng. Comput. Sci. 19(1), 97–107 (2011)

    Google Scholar 

  2. Galloway, M.M.: Texture analysis using gray level run lengths. Comput. Graph. Image Process. 4(2), 172–179 (1975)

    Article  Google Scholar 

  3. Bovik, A.C., Gopal, N., Emmoth, T., Restrepo, A.: Localized measurement of emergent image frequencies by Gabor wavelets. IEEE Trans. Inf. Theory 38(2), 691–712 (1992)

    Article  Google Scholar 

  4. Ojala, T., Pietikinen, M., Harwood, D.: A comparative study of texture measures with classification based on featured distributions. Pattern Recogn. 29(1), 51–59 (1996)

    Article  Google Scholar 

  5. Jabid, T., Kabir, M.H., Chae, O.: Local directional pattern (LDP) for face recognition. In: Proceedings of the IEEE International Conference on Consumer Electronics (ICCE), pp. 329–330, January 2010

    Google Scholar 

  6. Wang, X., Gong, H., Zhang, H., Li, B., Zhuang, Z.: Palmprint identification using boosting local binary pattern. In: Proceedings of 18th International Conference on Pattern Recognition (ICPR 2006), vol. 3, pp. 503–506, August 2006

    Google Scholar 

  7. 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)

    Article  MATH  Google Scholar 

  8. Zhang, G., Huang, X., Li, S.Z., Wang, Y., Wu, X.: Boosting local binary pattern (LBP)-based face recognition. In: Li, S.Z., Lai, J., Tan, T., Feng, G., Wang, Y. (eds.) Advances in Biometric Person Authentication. LNCS, vol. 3338, pp. 179–186. Springer, Berlin Heidelberg (2004). doi:10.1007/978-3-540-30548-4_21

    Chapter  Google Scholar 

  9. Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 971–987 (2002)

    Article  MATH  Google Scholar 

  10. Pietikinen, M., Hadid, A., Zhao, G., Ahonen, T.: Local binary patterns for still images. Computer Vision Using Local Binary Patterns. Computational Imaging and Vision, pp. 13–47. Springer, London (2011)

    Chapter  Google Scholar 

  11. Jabid, T., Kabir, M.H., Chae, O.: Robust facial expression recognition based on local directional pattern. ETRI J. 32(5), 784–794 (2010)

    Article  Google Scholar 

  12. Kabir, M.H., Jabid, T., Chae, O.: A local directional pattern variance (LDPv) based face descriptor for human facial expression recognition. In: Seventh IEEE International Conference Proceedings of Advanced Video and Signal Based Surveillance (AVSS), pp. 526–532, August 2010

    Google Scholar 

  13. Zhong, F., Zhang, J.: Face recognition with enhanced local directional patterns. Neurocomputing 119, 375–384 (2013)

    Article  Google Scholar 

  14. Kylberg, G.: Kylberg Texture Dataset v. 1.0. Centre for Image Analysis, Swedish University of Agricultural Sciences and Uppsala University (2011)

    Google Scholar 

  15. Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J.: Machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011)

    MathSciNet  MATH  Google Scholar 

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Correspondence to Jules-Raymond Tapamo .

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Shabat, A.M., Tapamo, JR. (2017). An Improved Scheme of Local Directional Pattern for Texture Analysis with an Application to Facial Expressions. In: Felsberg, M., Heyden, A., Krüger, N. (eds) Computer Analysis of Images and Patterns. CAIP 2017. Lecture Notes in Computer Science(), vol 10425. Springer, Cham. https://doi.org/10.1007/978-3-319-64698-5_15

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  • DOI: https://doi.org/10.1007/978-3-319-64698-5_15

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