Feature Representation Method Based on Kirsch Masks Filter for Face Recognition

Zhao Kui Li; Yan Wang

doi:10.4028/www.scientific.net/AMR.989-994.4209

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 989-994Feature Representation Method Based on Kirsch...

Feature Representation Method Based on Kirsch Masks Filter for Face Recognition

Abstract:

In this paper, a feature representation method based on Kirsch masks filter for face recognition is proposed. We firstly obtain eight direction images by performing Kirsch masks filter. For each direction image, the low-dimensional feature vector is computed by Linear Discriminant Analysisis. Then, a fusion strategy is used to combine different direction image according to their respective salience. Experimental results show that our methods significantly outperform popular methods such as Gabor features, Local Binary Patterns, Regularized Robust Coding (RRC), and achieve state-of-the-art performance for difficult problems such as illumination and occlusion-robust face recognition.

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Periodical:

Advanced Materials Research (Volumes 989-994)

Pages:

4209-4212

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.989-994.4209

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Online since:

July 2014

Authors:

Zhao Kui Li*, Yan Wang

Keywords:

Decision Level Fusion, Direction Image, Face Recognition, Kirsch Masks Filter, Linear Discriminant Analysis

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* - Corresponding Author

References

[1] M. Turk and A. Pentland, Eigenfaces for Recognition, , J. Cognitive Neuroscience, vol. 3, no. 1, pp.71-86, (1991).

Google Scholar

[2] P.N. Belhumeur, J.P. Hespanha, and D.J. Kriegman, Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection, IEEE Trans. Pattern Analysis and Machine Intelligences, vol. 19, no. 7, pp.711-720, July (1997).

DOI: 10.1109/34.598228

Google Scholar

[3] T. Ahonen, A. Hadid, and M. Pietikainen, Face Description with Local Binary Patterns: Application to Face Recognition, , IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 28, no. 12, pp.: 2037-2041, December (2006).

DOI: 10.1109/tpami.2006.244

Google Scholar

[4] L. Chengjun, Gabor-Based Kernel PCA with Fractional Power Polynomial Models for Face Recognition, , IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 26, no. 5, pp.572-581, May (2004).

DOI: 10.1109/tpami.2004.1273927

Google Scholar

[5] M. Yang, L. Zhang, J. Yang and D. Zhang, Regularized Robust Coding for Face Recognition, , IEEE Trans. on Image Processing, vol. 22, no. 5, pp.1753-1766, (2013).

DOI: 10.1109/tip.2012.2235849

Google Scholar

[6] A.M. Martinez and R. Benavente, The AR Face Database, , CVC technical report, (1998).

Google Scholar