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A Multiscale Method for HOG-Based Face Recognition

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9244))

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Abstract

Image representation is an important process in image classification, and there are many different methods for representing images. HOG (Histograms of Oriented Gradients) is a popular one which has been used in many applications including face recognition, pedestrian detection and palmprint recognition. In this paper, a novel method is presented to improve HOG-based image classification by using the multiscale features of images. For each image, multiple HOG feature vectors are extracted under different spatial dimensions (or ’scales’). These ’multiscale’ feature vectors are then fused into a distance function to calculate the distance between two images. Experiments have been conducted on ORL face database, AR face database and FERET face database. Results show the use of multiscale HOG features has led to significant improvement in performance over the use of single scale HOG features. Results also show that the nearest neighbour classifier equipped with our distance function is comparable to the well-known and widely-used benchmark classifier.

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References

  1. http://www.cl.cam.ac.uk/research/dtg/attarchive/facedatabase.html

  2. Ahmad, M.I., Ilyas, M.Z., Md Isa, M.N., Ngadiran, R., Darsono, A.M.: Information fusion of face and palmprint multimodal biometrics. In: 2014 IEEE Region 10 Symposium, pp. 635–639. IEEE (2014)

    Google Scholar 

  3. Bay, H., Tuytelaars, T., Van Gool, L.: SURF: Speeded Up Robust Features. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006, Part I. LNCS, vol. 3951, pp. 404–417. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  4. Belhumeur, P.N., Hespanha, J.P., Kriegman, D.J.: Eigenfaces vs. fisherfaces: Recognition using class specific linear projection. IEEE Transactions on Pattern Analysis and Machine Intelligence 19(7), 711–720 (1997)

    Google Scholar 

  5. Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. 1, pp. 886–893. IEEE (2005)

    Google Scholar 

  6. Dang, L., Bui, B., Vo, P.D., Tran, T.N., Le, B.H.: Improved hog descriptors. In: 2011 Third International Conference on Knowledge and Systems Engineering (KSE), pp. 186–189. IEEE (2011)

    Google Scholar 

  7. Déniz, O., Bueno, G., Salido, J., De la Torre, F.: Face recognition using histograms of oriented gradients. Pattern Recognition Letters 32(12), 1598–1603 (2011)

    Article  Google Scholar 

  8. Felzenszwalb, P.F., Girshick, R.B., McAllester, D., Ramanan, D.: Object detection with discriminatively trained part-based models. IEEE Transactions on Pattern Analysis and Machine Intelligence 32(9), 1627–1645 (2010)

    Google Scholar 

  9. Gao, Z., Ding, L., Xiong, C., Huang, B.: A robust face recognition method using multiple features fusion and linear regression. Wuhan University Journal of Natural Sciences 19(4), 323–327 (2014)

    Article  Google Scholar 

  10. Hou, C., Ai, H., Lao, S.: Multiview Pedestrian Detection Based on Vector Boosting. In: Yagi, Y., Kang, S.B., Kweon, I.S., Zha, H. (eds.) ACCV 2007, Part I. LNCS, vol. 4843, pp. 210–219. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  11. Huang, Z.-H., Li, W.-J., Wang, J., Zhang, T.: Face recognition based on pixel-level and feature-level fusion of the top-levels wavelet sub-bands. Information Fusion 22, 95–104 (2015)

    Article  Google Scholar 

  12. Jia, W., Rong-Xiang, H., Lei, Y.-K., Zhao, Y., Gui, J.: Histogram of oriented lines for palmprint recognition. IEEE Transactions on Systems, Man, and Cybernetics: Systems 44(3), 385–395 (2014)

    Article  Google Scholar 

  13. Liu, C., Wechsler, H.: Gabor feature based classification using the enhanced fisher linear discriminant model for face recognition. IEEE Transactions on Image Processing 11(4), 467–476 (2002)

    Article  Google Scholar 

  14. David, G.: Lowe. Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 60(2), 91–110 (2004)

    Article  Google Scholar 

  15. Martinez, A., Benavente, R.: The AR Face Database. CVC Tech. Report 24, Report 24, (1998)

    Google Scholar 

  16. Matas, J., Chum, O., Urban, M., Pajdla, T.: Robust wide-baseline stereo from maximally stable extremal regions. Image and Vision Computing 22(10), 761–767 (2004)

    Article  Google Scholar 

  17. Nikan, S., Ahmadi, M.: Local gradient-based illumination invariant face recognition using local phase quantisation and multi-resolution local binary pattern fusion. IET Image Processing 9(1), 12–21 (2014)

    Article  Google Scholar 

  18. Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(7), 971–987 (2002)

    Article  Google Scholar 

  19. Jonathon Phillips, P., Moon, H., Rizvi, S.A., Rauss, P.J.: The feret evaluation methodology for face-recognition algorithms. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(10), 1090–1104 (2000)

    Google Scholar 

  20. Pong, K.-H., Lam, K.-M.: Multi-resolution feature fusion for face recognition. Pattern Recognition 47(2), 556–567 (2014)

    Article  Google Scholar 

  21. Satpathy, A., Jiang, X., Eng, H.-L.: Human detection by quadratic classification on subspace of extended histogram of gradients. IEEE Transactions on Image Processing 23(1), 287–297 (2014)

    Article  MathSciNet  Google Scholar 

  22. Swets, D.L., Weng, J.J.: Using discriminant eigenfeatures for image retrieval. IEEE Transactions on Pattern Analysis & Machine Intelligence 8, 831–836 (1996)

    Google Scholar 

  23. Turk, M.A., Pentland, A.P.: Face recognition using eigenfaces. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 1991, pp. 586–591. IEEE (1991)

    Google Scholar 

  24. Watanabe, T., Ito, S., Yokoi, K.: Co-occurrence histograms of oriented gradients for human detection. IPSJ Transactions on Computer Vision and Applications 2, 39–47 (2010)

    Article  Google Scholar 

  25. Wei, X., Wang, H., Guo, G., Wan, H.: A General Weighted Multi-scale Method for Improving LBP for Face Recognition. In: Hervás, R., Lee, S., Nugent, C., Bravo, J. (eds.) UCAmI 2014. LNCS, vol. 8867, pp. 532–539. Springer, Heidelberg (2014)

    Google Scholar 

  26. Yang, J., Zhang, D., Frangi, A.F., Yang, J.-Y.: Two-dimensional pca: a new approach to appearance-based face representation and recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 26(1), 131–137 (2004)

    Google Scholar 

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Author information

Authors and Affiliations

  1. School of Computing and Mathematics, University of Ulster at Jordanstown, Newtownabbey, UK

    Hui Wang

  2. Key Lab of Network Security and Cryptology, School of Mathematics and Computer Science, Fujian Normal University, Fuzhou, People’s Republic of China

    Xin Wei, Gongde Guo & Huan Wan

Authors
  1. Xin Wei
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  2. Gongde Guo
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  3. Hui Wang
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  4. Huan Wan
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Corresponding author

Correspondence to Hui Wang .

Editor information

Editors and Affiliations

  1. University of Portsmouth, Portsmouth, United Kingdom

    Honghai Liu

  2. Tokyo Metropolitan University, Tokyo, Japan

    Naoyuki Kubota

  3. Shanghai Jiao Tong University, Shanghai, China

    Xiangyang Zhu

  4. Karlsruhe Institute of Technology, Karlsruhe, Germany

    Rüdiger Dillmann

  5. University of Portsmouth, Portsmouth, United Kingdom

    Dalin Zhou

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Wei, X., Guo, G., Wang, H., Wan, H. (2015). A Multiscale Method for HOG-Based Face Recognition. In: Liu, H., Kubota, N., Zhu, X., Dillmann, R., Zhou, D. (eds) Intelligent Robotics and Applications. ICIRA 2015. Lecture Notes in Computer Science(), vol 9244. Springer, Cham. https://doi.org/10.1007/978-3-319-22879-2_49

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  • DOI: https://doi.org/10.1007/978-3-319-22879-2_49

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-22878-5

  • Online ISBN: 978-3-319-22879-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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