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A v-Hog Tensor Based Discriminant Analysis for Small Size Face Recognition

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Advances in Signal Processing and Intelligent Recognition Systems (SIRS 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 968))

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Abstract

Apart from Illumination, Pose and Expression variations, low dimension is also a primary concern that spiflicate the performance of face recognition system. This work distils to applying v-Hog Tensor discriminant analysis on small sized face image to yield good result in terms of correctness rate. Firstly the face image is mapped on to w-quintuple Colorspace to effectively interpret information existing in the image. Further discriminant features are extracted out of Tensor plane to bore on the confounded image due to reduction of image size. To exhibit the beauty of the feature, v-Hog [22] is adopted. The obtained features are further mapped to a lower dimension space for efficient face recognition. In this work the effect of fSVD [17] with bias is also considered to fortify the recognition system. Finally, for classification five different similarity measures are used to obtain an average correctness rate.

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References

  1. Ghinea, G., Kannan, R., Kannaiyan, S.: Gradient-orientation-based PCA subspace for novel face recognition. IEEE Access 2, 914–920 (2014)

    Article  Google Scholar 

  2. Turk, M., Pentland, A.: Eigenfaces for recognition. J. Cogn. Neurosci. 3(1), 71–86 (1991)

    Article  Google Scholar 

  3. Zhang, D., Zhou, Z.-H.: (2D) 2PCA: two-directional two-dimensional PCA for efficient face representation and recognition. Neurocomputing 69(1), 224–231 (2005)

    Article  Google Scholar 

  4. Devi, B.J., Veeranjaneyulu, N., Kishore, K.V.K.: A novel face recognition system based on combining eigenfaces with fisher faces using wavelets. Procedia Comput. Sci. 2, 44–51 (2010)

    Article  Google Scholar 

  5. Wang, F., Wang, J., Zhang, C., Kwok, J.: Face recognition using spectral features. Pattern Recognit. 40(10), 2786–2797 (2007)

    Article  Google Scholar 

  6. Kang, C., Liao, S., Xiang, S., Pan, C.: Kernel sparse representation with pixel-level and region-level local feature kernels for face recognition. Neurocomputing 133, 141–152 (2014)

    Article  Google Scholar 

  7. Krisshna, N.L.A., Kadetotad Deepak, V., Manikantan, K., Ramachandran, S.: Face recognition using transform domain feature extraction and PSO-based feature selection. Appl. Soft Comput. 22, 141–161 (2014)

    Article  Google Scholar 

  8. Perez, C.A., Cament, L.A., Castillo, L.E.: Methodological improvement on local Gabor face recognition based on feature selection and enhanced Borda count. Pattern Recognit. 44(4), 951–963 (2011)

    Article  Google Scholar 

  9. Zhao, X., He, Z., Zhang, S., Kaneko, S., Satoh, Y.: Robust face recognition using the GAP feature. Pattern Recognit. 46(10), 2647–2657 (2013)

    Article  Google Scholar 

  10. Li, D., Tang, X., Pedrycz, W.: Face recognition using decimated redundant discrete wavelet transforms. Mach. Vis. Appl. 23(2), 391–401 (2012)

    Article  Google Scholar 

  11. Meng, J., Zhang, W.: Volume measure in 2DPCA-based face recognition. Pattern Recognit. Lett. 28(10), 1203–1208 (2007)

    Article  Google Scholar 

  12. Wang, Z., Sun, X.: Multiple kernel local Fisher discriminant analysis for face recognition. Signal Process. 93(6), 1496–1509 (2013)

    Article  Google Scholar 

  13. Sharma, P., Arya, K.V., Yadav, R.N.: Efficient face recognition using wavelet-based generalized neural network. Signal Process. 93(6), 1557–1565 (2013)

    Article  Google Scholar 

  14. Wu, S.: Quaternion-based improved LPP method for color face recognition. Opt. Int. J. Light. Electron Opt. 125(10), 2344–2349 (2014)

    Article  Google Scholar 

  15. Aifanti, N., Delopoulos, A.: Linear subspaces for facial expression recognition. Signal Process. Image Commun. 29(1), 177–188 (2014)

    Article  Google Scholar 

  16. Hu, H.: ICA-based neighborhood preserving analysis for face recognition. Comput. Vis. Image Underst. 112(3), 286–295 (2008)

    Article  Google Scholar 

  17. Bhaskar, B., Mahantesh, K., Geetha, G.P.: An investigation of fSVD and ridgelet transform for illumination and expression invariant face recognition. In: El-Alfy, E.-S.M., Thampi, S.M., Takagi, H., Piramuthu, S., Hanne, T. (eds.) Advances in Intelligent Informatics. AISC, vol. 320, pp. 31–38. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-11218-3_4

    Chapter  Google Scholar 

  18. 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 (2005)

    Google Scholar 

  19. Tzimiropoulos, G., Zafeiriou, S., Pantic, M.: Principal component analysis of image gradient orientations for face recognition. In: IEEE International Conference on Automatic Face and Gesture Recognition and Workshops (FG 2011), pp. 553–558, March 2011

    Google Scholar 

  20. Yang, J., et al.: Two-dimensional PCA: a new approach to appearance-based face representation and recognition. IEEE Trans. Pattern Anal. Mach. Intell. 26(1), 131–137 (2004)

    Article  MathSciNet  Google Scholar 

  21. Torres, L., Reutter, J.-Y., Lorente, L.: The importance of the color information in face recognition. In: Proceedings of the 1999 International Conference on Image Processing, ICIP 1999, vol. 3. IEEE (1999)

    Google Scholar 

  22. Belavadi, B., Mahendra Prashanth, K.V.: An exploration of v-HOG on w-Quartette space for multi face recognition issues. In: Sa, P.K., Sahoo, M.N., Murugappan, M., Wu, Y., Majhi, B. (eds.) Progress in Intelligent Computing Techniques: Theory, Practice, and Applications. AISC, vol. 518, pp. 219–226. Springer, Singapore (2018). https://doi.org/10.1007/978-981-10-3373-5_21

    Chapter  Google Scholar 

  23. Wang, D., Wang, X., Kong, S.: Integration of multi-feature fusion and dictionary learning for face recognition. Image Vis. Comput. 31(12), 895–904 (2013)

    Article  Google Scholar 

  24. Hou, Y.-F., Pei, W.-J., Chong, Y.-W., Zheng, C.-H.: Eigenface-based sparse representation for face recognition. In: Huang, D.-S., Jo, K.-H., Zhou, Y.-Q., Han, K. (eds.) ICIC 2013. LNCS (LNAI), vol. 7996, pp. 457–465. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-39482-9_53

    Chapter  Google Scholar 

  25. Bhaskar, B., Anushree, P.S., Divya Shree, S., Mahendra Prashanth, K.V.: Quantitative analysis of kernel principal components and kernel fishers based face recognition algorithms using hybrid gaborlets. Procedia Comput. Sci. 58, 342–347 (2015)

    Article  Google Scholar 

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Authors and Affiliations

  1. SJB Institute of Technology, Affiliated to VTU, Bengaluru, Karnataka, India

    Belavadi Bhaskar & K. V. Mahendra Prashanth

Authors
  1. Belavadi Bhaskar
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  2. K. V. Mahendra Prashanth
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Correspondence to Belavadi Bhaskar .

Editor information

Editors and Affiliations

  1. Indian Institute of Information Technology and Management, Kerala, India

    Sabu M. Thampi

  2. Department of Computer and Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, FL, USA

    Oge Marques

  3. Department of Electrical and Computer Engineering, Ryerson University, Toronto, ON, Canada

    Sri Krishnan

  4. Department of Computer Science and Information Engineering, Providence University, Taichung, Taiwan

    Kuan-Ching Li

  5. University of Naples Federico II, Naples, Italy

    Domenico Ciuonzo

  6. Electrical Engineering Department, Indian Institute of Technology Patna, Patna, India

    Maheshkumar H. Kolekar

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Bhaskar, B., Mahendra Prashanth, K.V. (2019). A v-Hog Tensor Based Discriminant Analysis for Small Size Face Recognition. In: Thampi, S., Marques, O., Krishnan, S., Li, KC., Ciuonzo, D., Kolekar, M. (eds) Advances in Signal Processing and Intelligent Recognition Systems. SIRS 2018. Communications in Computer and Information Science, vol 968. Springer, Singapore. https://doi.org/10.1007/978-981-13-5758-9_11

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  • DOI: https://doi.org/10.1007/978-981-13-5758-9_11

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-5757-2

  • Online ISBN: 978-981-13-5758-9

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