© The Institution of Engineering and Technology
In practical systems, face recognition under unconstrained conditions is a very challenging task, where their input images are first pre-processed and initially aligned by a face detection algorithm. However, there are still some residual localisation errors after the initial alignment. If we do not take these errors into account, the recognition performance should be greatly degraded for most face recognition algorithms. Generally, when designing a practical face recognition system, we need to compromise the capability of residual error tolerance and the discriminating capability. Although it is feasible to apply an iterative alignment algorithm to fine-tune alignment, it will increase the computation load significantly. In this study, we propose an adaptive two-stage face recognition system consisting of two block-based recognition stages with a relatively larger cell size (i.e. the size of local regions) in the first stage to provide sufficient tolerance for geometric errors followed by a smaller one in the second stage to accurately evaluate a most probable candidate subset, which is adaptively determined according to the proposed confidence measure. In addition, an iterative gradient-based alignment algorithm is incorporated into the two-stage system to refine the alignment such that the recognition performance can be improved and the computation load can be saved simultaneously.
References
-
-
1)
-
S. Baker ,
I. Matthews
.
Lucas–Kanade 20 years on: a unifying framework.
Int. J. Comput. Vis.
,
221 -
255
-
2)
-
20. Yang, C.-H.T., Lai, S.-H., Chang, L.-W.: ‘Robust face image matching under illumination variations’, EURASIP J. Appl. Signal Process., 2004, 16, pp. 2533–2543 (doi: 10.1155/S1110865704410014).
-
3)
-
P.J. Phillips ,
H. Moon ,
S. Rizvi ,
P. Rauss
.
The FERET evaluation methodology for face recognition algorithms.
IEEE Trans. Pattern Anal. Mach. Intell.
,
10 ,
1090 -
1104
-
4)
-
10. Tola, E., Lepetit, V., Fua, P.: ‘DAISY: an efficient dense descriptor applied to wide-baseline stereo’, IEEE Trans. Pattern Anal. Mach. Intell., 2010, 32, (5), pp. 815–830 (doi: 10.1109/TPAMI.2009.77).
-
5)
-
22. Yang, A.Y., Zhou, Z., Balasubramanian, A.G., Sastry, S., Ma, Y.: ‘Fast ℓ1-minimization algorithms for robust face recognition’, IEEE Trans. Image Process., 2013, 22, (8), pp. 3234–3246 (doi: 10.1109/TIP.2013.2262292).
-
6)
-
17. Shan, S., Chang, Y., Gao, W., Cao, B.: ‘Curse of mis-alignment in face recognition: problem and a novel mis-alignment learning solution’. Proc. of the Sixth IEEE Conf. on Automatic Face and Gesture Recognition, 2004, pp. 314–320.
-
7)
-
23. Su, CY., Yang, JF.: ‘A two-stage low complexity face recognition system for face images with alignment errors’. Proc. IEEE Int. Symp. on Circuits and Systems (ISCAS), 2013, pp. 2131–2134.
-
8)
-
14. Albiol, A., Monzo, D., Martin, A., Sastre, J., Albiol, A.: ‘Face recognition using HOG-EBGM’, Pattern Recognit. Lett., 2008, 29, pp. 1537–1543 (doi: 10.1016/j.patrec.2008.03.017).
-
9)
-
13. Shan, S., Gao, W., Chang, Y., Cao, B., Yang, P.: ‘Review the strength of Gabor features for face recognition from the angle of its robustness to mis-alignment’. Proc. 17th Int. Conf. on Pattern Recognition (ICPR), 2004, vol 1, pp. 338–341.
-
10)
-
11)
-
W. Zhao ,
R. Chellappa ,
A. Rosenfeld ,
P.J. Phillips
.
Face recognition: a literature survey.
ACM Comput. Surv.
,
4 ,
399 -
458
-
12)
-
15. Hua, G., Akbarzadeh, A.: ‘A robust elastic and partial matching metric for face recognition’. Proc. of the 12th Int. Conf. on Computer Vision (ICCV), 2009, pp. 2082–2089.
-
13)
-
6. Jesorski, O., Kirchberg, K., Frischholz, R.: ‘Robust face detection using the Hausdorff distance’. Proc. of the Third Int. Conf. on Audio- and Video-Based Biometric Person Authentication, 2001, pp. 90–95.
-
14)
-
L. Wiskott ,
J.-M. Fellous ,
N. Kuiger ,
C. Von Der Malsburg
.
Face recognition by elastic bunch graph matching.
IEEE Trans. Pattern Anal. Mach. Intell.
,
7 ,
775 -
779
-
15)
-
9. Wagner, A., Wright, J., Ganesh, A., Zhou, Z., Mobahi, H., Ma, Y.: ‘Towards a practical face recognition system: robust alignment and illumination by sparse representation’, IEEE Trans. Pattern Anal. Mach. Intell., 2012, 34, (2), pp. 597–604 (doi: 10.1109/TPAMI.2011.112).
-
16)
-
10. Deniz, O., Bueno, G., Salido, J., la Torre, F.D.: ‘Face recognition using histograms of oriented gradients’. Pattern Recognition Letters, 2011, pp. 1598–1603.
-
17)
-
12. Ahonen, T., Hadid, A., Pietikäinen, M.: ‘Face recognition with local binary patterns’. Proc. of European Conf. on Computer Vision (ECCV), 2004, pp. 469–481.
-
18)
-
26. Hartley, R., Zisserman, A.: ‘Multiple view geometry in computer vision’ (Cambridge University Press, 2000).
-
19)
-
R. Szeliski
.
Image alignment and stitching: a tutorial.
Found. Trends in Comput. Graph. Comput. Vis.
,
1 ,
1 -
104
-
20)
-
A.M. Martinez
.
Recognizing imprecisely localized, partially occluded, and expression variant faces from a single sample per class.
IEEE Trans. Pattern Anal. Mach. Intell.
,
6 ,
748 -
763
-
21)
-
Z. Hafed ,
M. Levine
.
Face recognition using the discrete cosine transform.
Int. J. Comput. Vis.
,
3 ,
167 -
188
-
22)
-
P. Belhumeur ,
J. Hespanha ,
D. Kriegman
.
Eigenfaces vs fusherfaces: recognition using class specific linear projection.
IEEE Trans. Pattern Anal. Mach. Intell.
,
7 ,
711 -
720
-
23)
-
27. Kay, S.M.: ‘Fundamentals of statistical signal processing: estimation theory’ (Prentice-Hall, 1993), vol. 1, .
-
24)
-
M. Turk ,
A. Pentland
.
Eigenfaces for recognition.
J. Cogn. Neurosci.
,
71 -
86
-
25)
-
30. Sim, T., Baker, S., Bsat, M.: ‘The CMU pose, illumination, and expression (PIE) database’. Proc. IEEE Int. Conf. Automatic Face and Gesture Recognition, 2002, pp. 46–51.
-
26)
-
T. Ojala ,
M. Pietikǎinen ,
T. Mǎenpǎǎ
.
Multiresolution gray-scale and rotation invariant texture classification with local binary patterns.
IEEE Trans. Pattern Anal. Mach. Intell.
,
7 ,
971 -
987
-
27)
-
28)
-
P. Viola ,
M. Jones
.
Robust real-time face detection.
Int. J. Comput. Vis.
,
2 ,
137 -
154
-
29)
-
5. Zhang, T., Tang, Y.Y., Fang, B., Shang, Z., Liu, X.: ‘Face recognition under illumination using gradientfaces’, IEEE Trans. Image Process., 2009, 18, (11), pp. 2599–2606 (doi: 10.1109/TIP.2009.2028255).
-
30)
-
7. Urschler, M., Storer, M., Bischof, H., Birchbauer, J.: ‘Robust facial component detection for face alignment applications’, in Roth, P., Mauthner, T., Pock, T. (Eds.): ‘Visual Learning, Proc. of the 33rd AAPR Workshop’, (Austrian Computer Society, 2009), vol. 254, pp. 61–72.
-
31)
-
9. Dalal, N., Triggs, B.: ‘Histograms of oriented gradients for human detection’. Proc. IEEE Conf. Computer Vision and Pattern Recognition, June 2005, vol. 2, pp. 886–893.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-cvi.2013.0281
Related content
content/journals/10.1049/iet-cvi.2013.0281
pub_keyword,iet_inspecKeyword,pub_concept
6
6