Multimedia Tools and Applications

, Volume 76, Issue 5, pp 6641–6661 | Cite as

Converted-face identification: using synthesized images to replace original images for recognition

  • Changbin Shao
  • Xiaoning Song
  • Xin Shu
  • Xiao-Jun Wu
Article
  • 221 Downloads

Abstract

The changes in appearance of faces, usually caused by pose, expression and illumination variations, increase data uncertainty in the task of face recognition. Insufficient training samples cannot provide abundant multi-view observations of a face. To address this issue, many pioneering works focus on generating virtual training images for better recognition performance. However, the issue also exists in a test set where a test image only conveys a split-second representation of a face and cannot cover more comprehensive features. In this paper, we propose a new face synthesis method for face recognition. In the proposed pipeline, we synthesize a virtual image using both the original image and its corresponding mirror one. Note that, we apply this technique both to the training and test sets. Then we use the newly generated training and test images to replace the original ones for face recognition. The aim is to increase the similarity between a test image and its corresponding intra-class training images. This proposed method is effective and computationally efficient. In order to verify this, we tested our system using multiple face recognition methods in terms of the recognition accuracy, based on either the synthesized images or original images. The methods used in the paper include statistical subspace learning algorithms and representation-based classification approaches. Experimental results obtained on FERET, ORL, GT, PIE and LFW show that the proposed approach improves the face recognition accuracy, especially on faces with left-right pose variations.

Keywords

Data uncertainty Mirror image Virtual image Representation-based classification Subspace learning Face recognition 

References

  1. 1.
    Belhumeour PN, Hespanha JP, Kriegman DJ (1997) Eigenfaces versus fisherfaces: recognition using class specific linear projection. IEEE Trans Pattern Anal Mach Intell 19(7):711–720CrossRefGoogle Scholar
  2. 2.
    Beymer D, Poggio T (1995) Face recognition from one example view. Proc Fifth Int Conf Comput Vision 500–507Google Scholar
  3. 3.
    Deng WH, Hu JN, Guo J, Cai WD, Feng DG (2010) Robust, accurate and efficient face recognition from a single training image: a uniform pursuit approach. Pattern Recogn 43:1748–1762CrossRefMATHGoogle Scholar
  4. 4.
    Deng W, Hu J, Guo J (2012) Extended SRC: Undersampled face recognition via intraclass variant dictionary. Pattern Anal Mach Intell, IEEE Trans 34(9):1864–1870CrossRefGoogle Scholar
  5. 5.
    Ekman P, Hager JC, Friesen WV (1981) The symmetry of emotional and deliberate facial actions. Psychophysiology 18(2):101–106CrossRefGoogle Scholar
  6. 6.
    Jian M, Lam KM, Dong J et al. (2011) Illumination compensation and enhancement for face recognition. Proc Asia–Pacific Sign Inform Process Assoc Ann Summit Conf (APSIPA ASC’2011), paper Wed-AM.RS6Google Scholar
  7. 7.
    Liu C, Wechsler H (2002) Gabor feature based classification using the enhanced fisher linear discriminant model for face recognition. IEEE Trans Imag Process 11(4):467–476CrossRefGoogle Scholar
  8. 8.
    Meng Y (2012) “Regularized robust coding for face recognition.” IEEE Trans Image Process: Publ IEEE Sign Process SocGoogle Scholar
  9. 9.
    Naseem I, Togneri R, Bennamoun M (2010) Linear regression for face recognition. IEEE Trans Pattern Anal Mach Intell 32(11):2106–2112CrossRefGoogle Scholar
  10. 10.
    Saad E-SM (2006) Frontal-view face detection in the presence of skin-tone regions using a new symmetry approach. J Comput Sci Technol 6Google Scholar
  11. 11.
    Saber E, Murat Tekalp A (1998) Frontal-view face detection and facial feature extraction using color, shape and symmetry based cost functions. Pattern Recognit Lett 19(8):669–680CrossRefMATHGoogle Scholar
  12. 12.
    Saha S, Sanghamitra B (2007) A symmetry based face detection technique. Proc IEEE WIE Natl Symposium Emerg TechnolGoogle Scholar
  13. 13.
    Sharma A, Dubey A, Tripathi P, Kumar V (2010) Pose invariant virtual classifiers from single training image using novel hybrid-eigenfaces. Neurocomputing 73(10–12):1868–1880CrossRefGoogle Scholar
  14. 14.
    Sugiyama M, Roweis S (2007) “Dimensionality reduction of multimodal labeled data by local Fisher discriminant analysis”. J Mach Learn Res 1027–1061Google Scholar
  15. 15.
    Swets DL, Weng JJ (1996) Using discriminant eigenfeatures for image retrieval. IEEE Trans Pattern Anal Mach Intell 8:831–836CrossRefGoogle Scholar
  16. 16.
    Swets DL, Weng JJ (1996) Using discriminant eigenfeatures for image retrieval. IEEE Trans Pattern Anal Mach Intell 18(8):831–836CrossRefGoogle Scholar
  17. 17.
    Tan X, Chen S, Zhou Z-H, Zhang F (2006) Face recognition from a single image per person: a survey. Pattern Recogn 39(9):1725–1745CrossRefMATHGoogle Scholar
  18. 18.
    Tang D, Zhu N, Yu F, Chen W, Tang T (2014) A novel sparse representation method based on virtual samples for face recognition. Neural Comput & Applic 24(3–4):513–519CrossRefGoogle Scholar
  19. 19.
    Turk M, Pentland A (1991) Eigenfaces for recognition. J Cogn Neurosci 3(1):71–86CrossRefGoogle Scholar
  20. 20.
    Vetter T (1998) Synthesis of novel views from a single face image. Int J Comput Vis 28(2):102–116CrossRefGoogle Scholar
  21. 21.
    Wang SJ, Yang J, Sun MF, Peng XJ, Sun MM, Zhou CG (2012) Sparse tensor discriminant color space for face verification. IEEE Trans Neural Netw Learn Syst 23(6):876–888CrossRefGoogle Scholar
  22. 22.
    Wright J (2009) “Robust face recognition via sparse representation.”. Pattern Anal Mach Intell, IEEE Trans 31(2):210–227CrossRefGoogle Scholar
  23. 23.
    Wright J et al (2010) Sparse representation for computer vision and pattern recognition. Proc IEEE 98(6):1031–1044CrossRefGoogle Scholar
  24. 24.
    Xu Y (2014) Integrating conventional and inverse representation for face recognition. Cybernet, IEEE Trans 44.10:1738–1746Google Scholar
  25. 25.
    Xu Y, Fang XZ, Li XL, Yang J, You J, Liu H, Teng SH (2014) Data uncertainty in face recognition. IEEE Trans Cybernet 44(10):1950–1961CrossRefGoogle Scholar
  26. 26.
    Xu Y, Jin Z (2008) Down-sampling face images and low-resolution face recognition. Proc 3rd Int Conf Innov Comput Inform Control 392–395Google Scholar
  27. 27.
    Xu Y, Li XL, Yang J, Zhang D (2014) Integrate the original face image and its mirror image for face recognition. Neurocomputing 131:191–199CrossRefGoogle Scholar
  28. 28.
    Xu Y, Zhang D, Jin Z, Li M, Yang JY (2006) A fast kernel-based nonlinear discriminant analysis for multi-class problems. Pattern Recogn 39(6):1026–1033CrossRefMATHGoogle Scholar
  29. 29.
    Xu Y, Zhang D, Yang J, Yang JY (2011) A two-phase test sample sparse representation method for use with face recognition, IEEE Trans. Circuits Syst. Video Technol 21(9):1255–1262MathSciNetGoogle Scholar
  30. 30.
    Xu Y, Zhu X, Li Z, Liu G et al (2013) Using the original and ‘symmetrical face’ training samples to perform representation based two-step face recognition. Pattern Recogn 46(4):1151–1158CrossRefGoogle Scholar
  31. 31.
    Yang J, Yang JY (2003) Why can LDA be performed in PCA transformed space. Pattern Recogn 36(2):563–566CrossRefGoogle Scholar
  32. 32.
    Yang J, Zhang D, Frangi AF, Yang JY (2004) Two-dimensional PCA: a new approach to appearance-based face representation and recognition. IEEE Trans Pattern Anal Mach Intell 26(1):131–137CrossRefGoogle Scholar
  33. 33.
    Zhang L (2011) Sparse representation or collaborative representation: which helps face recognition?. Proc ICCVGoogle Scholar
  34. 34.
    Zhang T, Li XF, Guo RZ (2014) Producing virtual face images for single sample face recognition. Optik 125:5017–5024CrossRefGoogle Scholar
  35. 35.
    Zhao W, Chellappa R, Phillips PJ et al (2003) Face recognition: a literature survey [J]. Acm Comput Surv (CSUR) 35(4):399–458CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Changbin Shao
    • 1
  • Xiaoning Song
    • 2
    • 3
  • Xin Shu
    • 1
  • Xiao-Jun Wu
    • 2
  1. 1.School of Computer Science and EngineeringJiangsu University of Science and TechnologyZhenjiangChina
  2. 2.School of Internet of Things EngineeringJiangnan UniversityWuxiChina
  3. 3.Centre for Vision, Speech and Signal ProcessingUniversity of SurreyGuildfordUK

Personalised recommendations