Feature scalability for a low complexity face recognition with unconstrained spatial resolution (original) (raw)

Abstract

Automatic face recognition (FR) based applications in low computing power constrained systems, such as mobile and smart camera, have become particularly interesting topic in recent years. In this context, we present computationally efficient FR framework underpinning the so-called feature scalability algorithm. The proposed framework aims at implementing robust FR systems under low-computing power restriction and varying face resolution. Key beneficial property of our proposed FR framework based on feature scalability is to require low computational complexity without sacrificing a level of FR performance. To do this, using feature scalability algorithm enables to directly estimate the features (from pre-enrolled gallery images) that are well matched with the feature of an input probe image with different resolution (generally lower resolution) without any complex process. In addition, our method is helpful for relieving storage shortage problem as it does not require a large amount of training and gallery images with different face resolutions. Results show that our proposed feature scalability algorithm can be seamlessly embedded into state-of-the-art feature extraction methods extensively used for FR by achieving impressive recognition performance. Also, according to the results on computational complexity measurement, the proposed method is proven to be useful for substantially saving FR operation time.

Access this article

Log in via an institution

Subscribe and save

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

Notes

References

  1. Ahonen T, Hadid A, Pietikainen M (2006) Face description with local binary patterns: application to face recognition. IEEE Trans Pattern Anal Mach Intell 28(12):2037–2041
    Article MATH Google Scholar
  2. Belhumeur PN, Hesphanha JP, Kriegman DJ (1997) Eigenfaces vs. Fisherfaces: Recognition using class specific linear projection. IEEE Trans Pattern Anal Mach Intell 9(7):711–720
    Article Google Scholar
  3. Boom BJ, Beumer GM, Spreeuwers LJ, Veldhuis RNJ (2006) The effect of image resolution on the performance of a face recognition system. In: IEEE International Conference Control, Automation Robotics and Vision
  4. Cardinaux F, Sanderson C, Bengio S (2006) User authentication via adapted statistical models of face images. IEEE Trans Signal Process 54(1):361–373
    Article Google Scholar
  5. Choi K, Toh KA, Byun H (2011) Realtime training on mobile devices for face recognition applications. Pattern Recognit 44(2):386–400
    Article Google Scholar
  6. Choi JY, Neve WD, Plataniotis KN, Ro YM (2011) Collaborative face recognition for improved face annotation in personal photo collections shared on online social networks. IEEE Trans Multimedia 13(1):14–28
    Article Google Scholar
  7. Choi JY, Ro YM, Plataniotis KN (2011) A comparative study of preprocessing mismatch effects in color image based face recognition. Pattern Recogn 44(10):412–430
    Article Google Scholar
  8. Choi JY, Ro YM, Plataniotis KN (2008) Feature subspace determination in video-based mismatches face recognition. In: IEEE International Conference Automatic Face & Gesture (FG) Recognition, pp 1–6
  9. Choi JY, Ro YM, Plataniotis KN (2009) Color face recognition for degraded face images. IEEE Trans Syst Man Cybern Part B Cybern 39(5):1217–1230
    Article Google Scholar
  10. Choi JY, Ro YM, Plataniotis KN (2012) Color local texture features for color face recognition. IEEE Trans Image Process 21(3):1366–1380
    Article MathSciNet Google Scholar
  11. Cover TM, Thomas JA (2005) Elements of Information Theory, 2nd Ed. John Wiley & Sons Inc
  12. Duda RO, Hart PE, Stork DG (2000) Pattern Classification, 2nd Ed. Wiley-Interscience
  13. Freeman WT, Jones TR, Pasztor EC (2002) Example-based super-resolution. IEEE Comput Graph Appl 22(2):55–65
    Article Google Scholar
  14. Grgic M, Delac K, Grgic S (2011) SCface — surveillance cameras face database. Multimedia Tools Applications 51(3):863–879
    Article Google Scholar
  15. Gunturk BK, Batur AU, Altunbasak Y, Hayes MH, Mersereau RM (2003) Eigenface-domain super-resolution for face recognition. IEEE Trans Image Process 12 (5):597–606
    Article Google Scholar
  16. Hadid A, Pietikainen M (2004) From still image to video-based face recognition: An experimental analysis. In: IEEE International Conference Automatic Face & Gesture (FG) Recognition, pp 813– 818
  17. Hua G, Yang M-H, Learned-Miller E, Ma Y, Turk M, Kriegman DJ, Huang TS (2011) Introduction to the special section on real-world face recognition. IEEE Trans Pattern Anal Mach Intell 33(10):1921– 1924
    Article Google Scholar
  18. Jia Y, Huang C, Darrell T (2012) Beyond spatial pyramids: receptive field learning for pooled image features. IEEE Conf Comput Vis Pattern Recognit (CVPR):3370–3377
  19. Kim CH, Seong SM, Lee JA, Kim LS (2003) Winscale: an image-scaling algorithm using an area pixel model. IEEE Trans Circuits Syst Video Technol 13(6):549–553
    Article Google Scholar
  20. Lee SH, Choi JY, Ro YM, Plataniotis KN (2012) Local color vector binary patterns from multichannel face images for face recognition. IEEE Trans Image Process 21(4):2347– 2353
    Article MathSciNet Google Scholar
  21. Li B, Chang H, Shan S, Chen X (2010) Low-resolution face recognition via coupled locality preserving mappings. IEEE Signal Process Lett 17(1):20–23
    Article Google Scholar
  22. Liu C, Shum HY, Freeman WT (2007) Face hallucination: theory and practice. Int J Comput Vis 75(1):115–134
    Article Google Scholar
  23. Liu C, Wechsler H (2002) Gabor feature based classification using the enhanced fisher linear discriminant model for face recognition. IEEE Trans Image Process 11(4):467–476
    Article Google Scholar
  24. Lu J, Plataniotis KN, Venetsanopoulos AN (2005) Regularization studies of linear discriminant analysis in small sample size scenarios with application to face recognition. Pattern Recogn Lett 26(2):181– 191
    Article Google Scholar
  25. Martinez AM, Benavente R (1998) The AR face database. CVC Technical Report
  26. Mishra G, Aung YL, Wu M, Lam SK, Srikanthan T (2013) Real-time image resizing hardware accelerator for object detection algorithms. Int’l Symposium on Electronic System Design. pp 98- 102
  27. Oh J, Kim G, Hong I, Lee S, Kim JY, Yoo HJ (2012) Low-power, real-time object-recognition processors for mobile vision systems. IEEE Microw Mag:38–50
  28. Rama Linga Reddy K, Babu GR, Kishore L (2011) Face recognition based on eigen features of multi scaled face components and artificial neural network. Int’l Journal of Security and Its Applications 5(3):23–44
    Google Scholar
  29. Samaria F, Harter A (1994) Parameterisation of a stochastic model for human face identification. In: 2nd IEEE Workshop on Applications of Computer Vision, pp 138–142
  30. Shan C, Gong S, McOwan PW (2009) Facial expression recognition based on local binary patterns: A comprehensive study. Image Vis Comput 27(6):803–816
    Article Google Scholar
  31. Sharif M, Khalid A, Raza M, Mohsin S (2011) Face recognition using Gabor filters. J Appl Comput Sci Math 11(5):53–57
    Google Scholar
  32. Su Y, Shan S, Chen X, Gao W (2009) Hierarchical ensemble of global and local classifiers for face recognition. IEEE Trans Image Process 18(8):1885–1896
    Article MathSciNet Google Scholar
  33. Turk MA, Pentland AP (1991) Eigenfaces for recognition. J Cogn Neurosci 3(1):71–86
    Article Google Scholar
  34. Wilman W, Zou W, Yuen PC (2012) Very low resolution face recognition problem. IEEE Trans Image Process 21(1):327–340
    Article MathSciNet Google Scholar
  35. Xie S, Shan S, Chen X, Chen J (2010) Fusing local patterns of Gabor magnitude and phase for face recognition. IEEE Trans Image Process 19(5):1349–1361
    Article MathSciNet Google Scholar
  36. Yang J, Wright J, Huang TS, Ma Y (2010) Image super-resolution via sparse representation. IEEE Trans Image Process 19(11):2861–2873
    Article MathSciNet Google Scholar
  37. Zhao W, Chellappa R, Phillips PJ, Rosenfeld A (2003) Face recognition: A literature survey. ACM Comput Surv 35(4):399–458
    Article Google Scholar
  38. Zou J, Ji Q, Nagy G (2007) A comparative study of local matching approach for face recognition. IEEE Trans Image Process 16(10):2617–2628
    Article MathSciNet Google Scholar

Download references

Acknowledgments

This work was supported by the ICT R&D program of MSIP/IITP. [14-824-09-002, Development of global multi-target tracking and event prediction techniques based on real-time large-scale video analysis].

Author information

Author notes

  1. Jae Young Choi
    Present address: Department of Biomedical Engineering, Jungwon University, Chungcheongbuk-do, Republic of Korea

Authors and Affiliations

  1. Department of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea
    Hyung-Il Kim, Jae Young Choi, Seung Ho Lee & Yong Man Ro
  2. Department of Biomedical Engineering, Jungwon University, 85 Munmu-ro Goesan-eup, Goesan-gun, Chungcheongbuk-do, 367-805, Republic of Korea
    Jae Young Choi

Authors

  1. Hyung-Il Kim
  2. Jae Young Choi
  3. Seung Ho Lee
  4. Yong Man Ro

Corresponding author

Correspondence toYong Man Ro.

Rights and permissions

About this article

Cite this article

Kim, HI., Choi, J.Y., Lee, S.H. et al. Feature scalability for a low complexity face recognition with unconstrained spatial resolution.Multimed Tools Appl 75, 6887–6908 (2016). https://doi.org/10.1007/s11042-015-2616-3

Download citation

Keywords