In real-world recognition applications, several poor situations such as varying environment, limited image information, and irregular status would lead performance degradation in recognition. To overcome the unexpected effects, the authors propose a generalised linear regression classification (GLRC) to fully use all the information of multiple components of input images. The proposed GLRC achieves the global adaptive weighted optimisation for linear regression classification (LCR), which can automatically use the distinction components for recognition. For colour identify recognition, the authors also suggest several similarity measures for the proposed GLRC to be tested in different colour spaces. Experiments are conducted on two object datasets and two face databases including Columbia Object Image Library-100, SOIL-47, SDUMLA-HMT and FEI. For performance comparisons, the proposed GLRC approach is compared with the contemporary popular methods including colour principal component analysis, colour linear discriminant analysis, colour canonical correlation analysis, LRC, robust LRC (RLRC), sparse representation classification (SRC), colour LRC, colour RLRC, and colour SRC. The simulation results demonstrate that the proposed GLRC method achieves the best performance in multi-component identity recognition.

References

1. 1)
  - 32. Nene, S.A., Nayar, S.K., Murase, H.: ‘Columbia object image library (COIL-100)’. Technical Report CUCS-006–96, 1996.
2. 2)
  - 2. Kim, J., Mowat, A., Poole, P., Kasabov, N.: ‘Linear and non-linear pattern recognition models for classification of fruit from visible-near infrared spectra’, Chemometr. Intell. Lab. Syst., 2000, 51, (2), pp. 201–216 (doi: 10.1016/S0169-7439(00)00070-8).
3. 3)
  - 22. Burges, C.J.C.: ‘A tutorial on support vector machines for pattern recognition’, Data Min. Knowl. Discov., 1998, 2, (2), pp. 121–167 (doi: 10.1023/A:1009715923555).
4. 4)
  - 27. Liu, Z., Liu, C.: ‘Fusion of the complementary discrete cosine features in the YIQ colour space for face recognition’, ACM Comput. Vis. Image Underst., 2008, 111, (3), pp. 249–262 (doi: 10.1016/j.cviu.2007.12.002).
5. 5)
  - 35. Available at //www.ee.surrey.ac.uk/Research/VSSP/demos/colour/soil47/.
6. 6)
  - 21. Jing, X., Li, S., Lan, C., Zhang, D., Yang, J., Liu, Q.: ‘Color image canonical correlation analysis for face feature extraction and recognition’, Signal Process., 2011, 91, (8), pp. 2132–2140 (doi: 10.1016/j.sigpro.2011.02.016).
7. 7)
  - 18. Chio, J.Y., Ro, Y.M., Plataniotis, K.N.: ‘Color face recognition for degraded face images’, IEEE Trans. Syst. Man Cybern. B Cybern., 2009, 39, (5), pp. 1217–1230 (doi: 10.1109/TSMCB.2009.2014245).
8. 8)
  - 8. Naseem, I., Togneri, R., Bennamoun, M.: ‘Linear regression for face recognition’, IEEE Trans. Pattern Anal. Mach. Intell., 2010, 32, (11), pp. 2106–2112 (doi: 10.1109/TPAMI.2010.128).
9. 9)
  - 23. Xie, J., Jiang, Y., Tsui, H.T.: ‘Segmentation of kidney from ultrasound images based on texture and shape priors’, IEEE Trans. Med. Imaging, 2005, 24, (1), pp. 45–57 (doi: 10.1109/TMI.2004.837792).
10. 10)
  - 30. Grant, M.C., Boyd, S.P.: ‘CVX.’ Available at http://cvxr.com/cvx/, 2013.
11. 11)
  - 36. Koubaroulis, D., Matas, J., Kittler, J.: ‘Evaluating colour-based object recognition algorithms using the SOIL-47 database’. Proc. Asian Conf. on Computer Vision, 2002, pp. 840–845.
12. 12)
  - 10. Naseem, I., Togneri, R., Bennamoun, M.: ‘Robust regression for face recognition’, Pattern Recogn., 2012, 45, (1), pp. 104–118 (doi: 10.1016/j.patcog.2011.07.003).
13. 13)
  - 38. Yin, Y., Liu, L., Sun, X.: ‘SDUMLA-HMT: A multimodal biometric database’, biometric recognitoin, (Springer Berlin Heidelberg, 2011), pp. 260–268.
14. 14)
  - 24. Groetsch, C.: ‘Generalized inverses of linear operators: representation and approximation’, 1977.
15. 15)
  - 34. Han, X.H., Chen, Y.W., Ruan, X.: ‘Multilinear supervised neighborhood embedding of a local descriptor tensor for scene/ object recognition’, IEEE Trans. Image Process., 2012, 21, (3), pp. 1314–1326 (doi: 10.1109/TIP.2011.2168417).
16. 16)
  - 1. Ullman, S., Basri, R.: ‘Recognition by linear combinations of models’, IEEE Trans. Pattern Anal. Mach. Intell., 1991, 13, (10), pp. 992–1006 (doi: 10.1109/34.99234).
17. 17)
  - 26. Chiang, J.H.: ‘Aggregating membership values by A choquet-fuzzy-integral based operator’, Fuzzy Sets Syst., 2000, 114, (3), pp. 369–375 (doi: 10.1016/S0165-0114(98)00145-6).
18. 18)
  - 28. Pinto, S.H., Sogal, S.S., Kumar, C.V.: ‘Improved face recognition with multilevel BTC using YCbCr colour space’, J. Emerging Technol. Adv. Eng., 2013, 3, (9), pp. 266–270.
19. 19)
  - 33. Chen, J.H., Chen, C.S.: ‘Object recognition based on image sequences by using inter-feature-line consistencies’, Pattern Recogn., 2004, 37, (9), pp. 1913–1923 (doi: 10.1016/j.patcog.2003.12.003).
20. 20)
  - 17. Chio, J.Y., Ro, Y.M., Plataniotis, K.N.: ‘Boosting color feature selection for color face recognition’, IEEE Trans. Image Process., 2011, 20, (5), pp. 1425–1434 (doi: 10.1109/TIP.2010.2093906).
21. 21)
  - 25. Hoaglin, D.C., Welsch, R.E.: ‘The hat matrix in regression and ANOVA’, Am. Stat., 1978, 32, (1), pp. 17–22.
22. 22)
  - 14. Huang, S.M., Yang, J.F.: ‘Unitary regression classification with total minimum projection error for face recognition’, IEEE Signal Process. Lett., 2013, 20, (5), pp. 443–446 (doi: 10.1109/LSP.2013.2250957).
23. 23)
  - 7. Turk, M., Pentland, A.: ‘Eigenfaces for recognition’, J. Cogn. Neurosci, 1991, 13, (1), pp. 71–86 (doi: 10.1162/jocn.1991.3.1.71).
24. 24)
  - 12. Huang, S.M., Yang, J.F.: ‘Improved principal component regression for face recognition under illumination variations’, IEEE Signal Process. Lett., 2012, 19, (4), pp. 179–182 (doi: 10.1109/LSP.2012.2185492).
25. 25)
  - 36. Wright, J., Yang, A., Ganesh, A., Sastry, S., Ma, Y.: ‘Robust face recognition via sparse representation’, IEEE Trans. Pattern Anal. Mach. Intell., 2009, 31, (2), pp. 210–227 (doi: 10.1109/TPAMI.2008.79).
26. 26)
  - 39. Available at http://fei.edu.br/~cet/facedatabase.html.
27. 27)
  - 28. Belhumeur, P., Hespanha, J., Kriegman, D.: ‘Eigenfaces vs. Fisherfaces: recognition using class specific linear projection’, IEEE Trans. Pattern Anal. Mach. Intell., 1997, 19, pp. 711–720 (doi: 10.1109/34.598228).
28. 28)
  - 13. Huang, S.M., Yang, J.F.: ‘Linear discriminant regression classification for face recognition’, IEEE Signal Process. Lett., 2013, 20, (1), pp. 91–94 (doi: 10.1109/LSP.2012.2230257).
29. 29)
  - 29. Yoo, S., Park, R.H., Sim, D.G.: ‘Investigation of colour spaces for face recognition’. Proc. IAPR Int. Conf. on Machine Vision Applications, 2007, pp. 106–109.
30. 30)
  - 11. Huang, S.M., Yang, J.F.: ‘Kernel linear regression for low resolution face recognition under variable illumination’. Proc. IEEE Int. Conf. on Acoustics, Speech and Signal Processing (ICASSP), 2012, pp. 1945–1948.
31. 31)
  - 15. Xu, Y., Zhang, D., Yang, J., Yang, J.Y.: ‘A two-phase test sample sparse representation method for use with face recognition’, IEEE Trans. Circuits Syst. Video Technol., 2011, 21, (9), pp. 1255–1262 (doi: 10.1109/TCSVT.2011.2138790).
32. 32)
  - 31. Available at http://www.cs.columbia.edu/CAVE/software/softlib/coil-100.php.
33. 33)
  - 19. Chio, J.Y., Ro, Y.M., Plataniotis, K.N.: ‘Color local texture features for color face recognition’, IEEE Trans. Image Process., 2012, 21, (3), pp. 1366–1380 (doi: 10.1109/TIP.2011.2168413).
34. 34)
  - 16. Zhang, L., Yang, M., Feng, X.: ‘Sparse representation or collaborative representation: which helps face recognition?’. Proc. IEEE Int. Conf. on Computer Vision, 2011, pp. 471–478.
35. 35)
  - 37. Available at http://mla.sdu.edu.cn/sdumla-hmt.html.
36. 36)
  - 6. Yang, M., Zhang, L., Shiu, C.K., Zhang, D.: ‘Robust kernel representation with statistical local features for face recognition’, IEEE Trans. Neural Netw. Learn. Syst., 2013, 24, (6), pp. 900–912 (doi: 10.1109/TNNLS.2013.2245340).
37. 37)
  - 22. Kotsia, I., Zafeiriou, S., Pitas, I.: ‘Testure and shape information fusion for facial expression and facial action uint recognition’, Pattern Recogn., 2008, 41, (3), pp. 833–851 (doi: 10.1016/j.patcog.2007.06.026).
38. 38)
  - 20. Yang, J., Liu, C., Zhang, L.: ‘space normalization: enhancing the discriminating power of color spaces for face recognition’, Pattern Recogn., 2010, 43, (4), pp. 1454–1466 (doi: 10.1016/j.patcog.2009.11.014).
39. 39)
  - 29. Yang, J., Zhang, D., Frangi, A., et al: ‘Two-dimensional PCA: a new approach to appearance-based face representation and recognition’, IEEE Trans. Pattern Anal. Mach. Intell., 2004, 6, pp. 131–137 (doi: 10.1109/TPAMI.2004.1261097).

Identity recognition based on generalised linear regression classification for multi-component images

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