Skip to main content
Log in

Explicit and implicit employment of edge-related information in super-resolving distant faces for recognition

  • Industrial and Commercial Application
  • Published:
Pattern Analysis and Applications Aims and scope Submit manuscript

Abstract

Edges and related features play significant role in discriminating face images. But those features are not enough informative when the face images are captured from a distance (e.g., video surveillance). Traditionally, those features are enhanced by super-resolving low-resolution grayscale face images. In this paper, we demonstrate a superior performance by directly considering such features (continuous gradient value, also known as edginess) in the super-resolution process. Edginess features are extracted using 1-D processing of image. This process is carried out along different directions to obtain partial evidences, which are combined to detect the person’s identity. Here, super-resolution of the face image and its recognition has been performed in sparse domain framework. The explicit usage of edginess feature in the proposed approach shows considerable improvement in both recognition performance as well as computational time, as only the patches related to strong edges are considered for super-resolution. In addition to that, the edginess feature gives improved recognition rate when it is preserved implicitly during super-resolution in grayscale domain.

This is a preview of subscription content, log in via an institution to check access.

Access this article

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

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

Notes

  1. Here, feature means edginess feature for edginess domain and grayscale values for grayscale domain.

  2. Here, we have considered all the angles with respect to x-axis in clockwise direction.

  3. Here, \(\bf {x}\) is representation of face image and it could be gray-level values or partial edge evidences.

  4. The edginess values are normalized to the range 0–255.

  5. Computational time is measured under same system condition for both types of SR.

References

  1. Aharon M, Elad M, Bruckstein A (2006) K-SVD: an algorithm for designing overcomplete dictionaries for sparse representation. IEEE Trans Signal Process 54(11):4311–4322. doi:10.1109/TSP.2006.881199

    Article  Google Scholar 

  2. 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. doi:10.1109/TPAMI.2006.244

    Article  MATH  Google Scholar 

  3. Belhumeur P, Hespanha J, Kriegman D (1997) Eigenfaces vs. Fisherfaces: recognition using class specific linear projection. IEEE Trans Pattern Anal Mach Intell 19(7):711–720. doi:10.1109/34.598228

    Article  Google Scholar 

  4. Biswas S, Aggarwal G, Flynn P, Bowyer K (2013) Pose-robust recognition of low-resolution face images. IEEE Trans Pattern Anal Mach Intell 35(12):3037–3049. doi:10.1109/TPAMI.2013.68

    Article  Google Scholar 

  5. Biswas S, Bowyer KW, Flynn PJ (2012) Multidimensional scaling for matching low-resolution face images. IEEE Trans Pattern Anal Mach Intell 34(10):2019–2030. doi:10.1109/TPAMI.2011.278

    Article  Google Scholar 

  6. Black JA Jr, Gargesha M, Kahol K, Kuchi P, Panchanathan S (2002) A framework for performance evaluation of face recognition algorithms. In: ITCOM, internet multimedia systems II,pp 163–174. doi:10.1117/12.473032

  7. Chen SS, Donoho DL, Saunders MA (2001) Atomic decomposition by basis pursuit. SIAM Rev 43(1):129–159. doi:10.1137/S003614450037906X

    Article  MathSciNet  MATH  Google Scholar 

  8. Daubechies I, Defrise M, De Mol C (2004) An iterative thresholding algorithm for linear inverse problems with a sparsity constraint. Commun Pure Appl Math 57(11):1413–1457. doi:10.1002/cpa.20042

    Article  MathSciNet  MATH  Google Scholar 

  9. Dong W, Zhang L, Shi G, Wu X (2011) Image deblurring and super-resolution by adaptive sparse domain selection and adaptive regularization. IEEE Trans Image Process 20(7):1838–1857. doi:10.1109/TIP.2011.2108306

    Article  MathSciNet  Google Scholar 

  10. Donoho DL (2006) For most large underdetermined systems of equations, the minimal \(l_1\)-norm near-solution approximates the sparsest near-solution. Commun Pure Appl Math 59(7):907–934. doi:10.1002/cpa.20131

    Article  MathSciNet  Google Scholar 

  11. Elad M (2010) Sparse and redundant representations: from theory to applications in signal and image processing. Springer, New York. doi:10.1007/978-1-4419-7011-4

    Book  MATH  Google Scholar 

  12. Emil Bilgazyev Boris Efraty SS, Kakadiaris I (2011) Sparse representation-based super resolution for face recognition at a distance. In: Proceedings of the British machine vision conference. BMVA Press, pp 52.1–52.11. doi:10.5244/C.25.52

  13. Engan K, Aase S, Hakon Husoy J (1999) Method of optimal directions for frame design. In: IEEE international conference on acoustics, speech, and signal processing, vol 5, pp 2443–2446. doi:10.1109/ICASSP.1999.760624

  14. Freeman W, Jones T, Pasztor E (2002) Example-based super-resolution. IEEE Comput Graphics Appl 22(2):56–65. doi:10.1109/38.988747

    Article  Google Scholar 

  15. Georghiades A, Belhumeur P, Kriegman D (2001) From few to many: illumination cone models for face recognition under variable lighting and pose. IEEE Trans Pattern Anal Mach Intell 23(6):643–660

    Article  Google Scholar 

  16. Grgic M, Delac K, Grgic S (2011) Scface surveillance cameras face database. Multimedia Tools Appl 51(3):863–879. doi:10.1007/s11042-009-0417-2

    Article  Google Scholar 

  17. Hou H, Andrews H (1978) Cubic splines for image interpolation and digital filtering. IEEE Trans Acoust Speech Signal Process 26(6):508–517. doi:10.1109/TASSP.1978.1163154

    Article  MATH  Google Scholar 

  18. Irani M, Peleg S (1991) Improving resolution by image registration. CVGIP Graph Models Image Process 53(3):231–239. doi:10.1016/1049-9652(91)90045-L

    Article  Google Scholar 

  19. Kan M, Shan S, Chang H, Chen X (2014) Stacked progressive auto-encoders (spae) for face recognition across poses. In: IEEE conference on computer vision and pattern recognition (CVPR), pp 1883–1890. doi:10.1109/CVPR.2014.243

  20. Kanemura A, Ichi Maeda S, Ishii S (2009) Superresolution with compound markov random fields via the variational EM algorithm. Neural Netw 22(7):1025–1034

    Article  MATH  Google Scholar 

  21. Keys R (1981) Cubic convolution interpolation for digital image processing. IEEE Trans Acoust Speech Signal Process 29(6):1153–1160. doi:10.1109/TASSP.1981.1163711

    Article  MathSciNet  MATH  Google Scholar 

  22. Lee K, Ho J, Kriegman D (2005) Acquiring linear subspaces for face recognition under variable lighting. IEEE Trans Pattern Anal Mach Intell 27(5):684–698

    Article  Google Scholar 

  23. Little D, Krishna S, Black J, Panchanathan S (2005) A methodology for evaluating robustness of face recognition algorithms with respect to variations in pose angle and illumination angle. IEEE Intl Conf Acoust Speech Signal Process 2:89–92. doi:10.1109/ICASSP.2005.1415348

    Google Scholar 

  24. 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. doi:10.1109/TIP.2002.999679

    Article  Google Scholar 

  25. Lui YM, Bolme D, Draper B, Beveridge J, Givens G, Phillips P (2009) A meta-analysis of face recognition covariates. In: IEEE 3rd international conference on biometrics: theory, applications, and systems, BTAS ’09, pp 1–8 (2009). doi:10.1109/BTAS.2009.5339025

  26. Mairal J, Bach F, Ponce J, Sapiro G, Zisserman A (2009) Non-local sparse models for image restoration. In: IEEE 12th international conference on computer Vision, pp 2272–2279. doi:10.1109/ICCV.2009.5459452

  27. Mairal J, Elad M, Sapiro G (2008) Sparse representation for color image restoration. IEEE Trans Image Process 17(1):53–69. doi:10.1109/TIP.2007.911828

    Article  MathSciNet  MATH  Google Scholar 

  28. Mallat S, Zhang Z (1993) Matching pursuits with time-frequency dictionaries. IEEE Trans Signal Process 41(12):3397–3415. doi:10.1109/78.258082

    Article  MATH  Google Scholar 

  29. Mandal S, Sao A (2013) Edge preserving single image super resolution in sparse environment. In: 20th IEEE international conference on image processing (ICIP), pp 967–971. doi:10.1109/ICIP.2013.6738200

  30. Marquina A, Osher S (2008) Image super-resolution by TV-regularization and bregman iteration. J Sci Comput 37(3):367–382. doi:10.1007/s10915-008-9214-8

    Article  MathSciNet  MATH  Google Scholar 

  31. Mu Y, Lo H, Ding W, Tao D (2014) Face recognition from multiple images per subject. In: Proceedings of the ACM international conference on multimedia, MM ’14. ACM, New York, pp 889–892. doi: 10.1145/2647868.2655054

  32. Nguyen H, Caplier A (2015) Local patterns of gradients for face recognition. IEEE Trans Inf Forensics Secur 10(8):1739–1751. doi:10.1109/TIFS.2015.2426144

    Article  Google Scholar 

  33. Park JS, Lee SW (2008) An example-based face hallucination method for single-frame, low-resolution facial images. IEEE Trans Image Process 17(10):1806–1816. doi:10.1109/TIP.2008.2001394

    Article  MathSciNet  Google Scholar 

  34. Park SC, Park MK, Kang MG (2003) Super-resolution image reconstruction: a technical overview. IEEE Signal Process Mag 20(3):21–36. doi:10.1109/MSP.2003.1203207

    Article  Google Scholar 

  35. Pelletier S, Cooperstock J (2012) Preconditioning for edge-preserving image super resolution. IEEE Trans Image Process 21(1):67–79. doi:10.1109/TIP.2011.2160188

    Article  MathSciNet  Google Scholar 

  36. Punnappurath A, Rajagopalan A, Taheri S, Chellappa R, Seetharaman G (2015) Face recognition across non-uniform motion blur, illumination, and pose. IEEE Trans Image Process 24(7):2067–2082. doi:10.1109/TIP.2015.2412379

    Article  MathSciNet  Google Scholar 

  37. Ramesh S, Palanivel S, Das S, Yegnanarayana B (2002) Eigenedginess vs. eigenhill, eigenface and eigenedge. In: European Signal Processing Conference. Toulose, France, pp 559–562 (2002)

  38. Rara H, Elhabian S, Ali A, Miller M, Starr T, Farag A (2009) Distant face recognition based on sparse-stereo reconstruction. In: 16th IEEE International Conference on Image Processing (ICIP), pp 4141–4144. doi:10.1109/ICIP.2009.5413467

  39. Rubinstein R, Bruckstein A, Elad M (2010) Dictionaries for sparse representation modeling. Proc IEEE 98(6):1045–1057. doi:10.1109/JPROC.2010.2040551

    Article  Google Scholar 

  40. Rudin LI, Osher S, Fatemi E (1992) Nonlinear total variation based noise removal algorithms. Phys D 60(1–4):259–268. doi:10.1016/0167-2789(92)90242-F

    Article  MathSciNet  MATH  Google Scholar 

  41. Sao A, Yegnanarayana B (2011) Laplacian of smoothed image as representation for face recognition. IEEE Int Workshop Inf Forensics Secur (WIFS) 2011:1–6. doi:10.1109/WIFS.2011.6123140

    Google Scholar 

  42. Sao AK, Yegnanarayana B, Vijaya Kumar B (2007) Significance of image representation for face verification. Signal Image Video Process 1:225–237. doi:10.1007/s11760-007-0016-5

    Article  MATH  Google Scholar 

  43. Shejin T, Sao A (2012) Significance of dictionary for sparse coding based face recognition. In: Proceedings of the international conference of the biometrics special interest group (BIOSIG), pp 1–6

  44. Stark H, Oskoui P (1989) High-resolution image recovery from image-plane arrays, using convex projections. J Opt Soc Am A 6(11):1715–1726

    Article  Google Scholar 

  45. Sun G, Shen Z (2010) Single image super-resolution via edge reconstruction and image fusion. In: Kim TH, Pal S, Grosky W, Pissinou N, Shih T, Ålzak D (eds) Signal processing and multimedia, communications in computer and information science, vol 123. Springer, Berlin-Heidelberg, pp 16–23. doi:10.1007/978-3-642-17641-8_3

  46. Tang S, Xiao L, Liu P, Zhang J, Huang L (2014) Edge and color preserving single image superresolution. J Electron Imaging 23(3):033002. doi:10.1117/1.JEI.23.3.033002

    Article  Google Scholar 

  47. Turk M, Pentland A (1991) Face recognition using eigenfaces. In: IEEE computer society conference on computer vision and pattern recognition, pp 586–591. doi:10.1109/CVPR.1991.139758

  48. Vidal R, Ma Y, Sastry S (2003) Generalized principal component analysis (GPCA). In: IEEE computer society conference on computer vision and pattern recognition, vol 1, pp I-621–I-628. doi:10.1109/CVPR.2003.1211411

  49. Vishnukumar S, Nair MS, Wilscy M (2014) Edge preserving single image super-resolution with improved visual quality. Signal Process 105:283–297. doi:10.1016/j.sigpro.2014.05.033

    Article  Google Scholar 

  50. Wang N, Tao D, Gao X, Li X, Li J (2013) Transductive face sketch-photo synthesis. IEEE Trans Neural Netw Learn Syst 24(9):1364–1376. doi:10.1109/TNNLS.2013.2258174

    Article  Google Scholar 

  51. Wang N, Tao D, Gao X, Li X, Li J (2014) A comprehensive survey to face hallucination. Int J Comput Vis 106(1):9–30. doi:10.1007/s11263-013-0645-9

    Article  Google Scholar 

  52. Wang X, Tang X (2009) Face photo-sketch synthesis and recognition. IEEE Trans Pattern Anal Mach Intell 31(11):1955–1967. doi:10.1109/TPAMI.2008.222

    Article  Google Scholar 

  53. Wei CP, Wang YC (2015) Undersampled face recognition via robust auxiliary dictionary learning. IEEE Trans Image Process 24(6):1722–1734. doi:10.1109/TIP.2015.2409738

    Article  MathSciNet  Google Scholar 

  54. Yang J, Wright J, Huang T, Ma Y (2008) Image super-resolution as sparse representation of raw image patches. In: IEEE conference on computer vision and pattern recognition, pp 1–8. doi:10.1109/CVPR.2008.4587647

  55. Yang J, Wright J, Huang T, Ma Y (2010) Image super-resolution via sparse representation. IEEE Trans Image Process 19(11):2861–2873. doi:10.1109/TIP.2010.2050625

    Article  MathSciNet  Google Scholar 

  56. Yang M, Huang D, Tsai C, Wang YF (2012) Self-learning of edge-preserving single image super-resolution via contourlet transform. In: Proceedings of the 2012 IEEE international conference on multimedia and Expo, ICME 2012, Melbourne, Australia, July 9–13, 2012, pp 574–579. doi:10.1109/ICME.2012.169

  57. Yuan Q, Zhang L, Shen H (2012) Multiframe super-resolution employing a spatially weighted total variation model. IEEE Trans Circuits Syst Video Technol 22(3):379–392. doi:10.1109/TCSVT.2011.2163447

    Article  Google Scholar 

  58. Zeyde R, Elad M, Protter M (2012) On single image scale-up using sparse-representations. In: Boissonnat JD, Chenin P, Cohen A, Gout C, Lyche T, Mazure ML, Schumaker L (eds) Curves and surfaces, lecture notes in computer science, vol 6920. Springer, Berlin Heidelberg, pp 711–730. doi:10.1007/978-3-642-27413-8_47

  59. Zhang K, Gao X, Tao D, Li X (2012) Single image super-resolution with non-local means and steering kernel regression. IEEE Trans Image Process 21(11):4544–4556. doi:10.1109/TIP.2012.2208977

    Article  MathSciNet  Google Scholar 

  60. Zhang L, Zhang H, Shen H, Li P (2010) A super-resolution reconstruction algorithm for surveillance images. Signal Process 90(3):848–859. doi:10.1016/j.sigpro.2009.09.002

    Article  MathSciNet  MATH  Google Scholar 

  61. Zhang X, Lam E, Wu E, Wong K (2008) Application of Tikhonov regularization to super-resolution reconstruction of brain MRI images. In: Gao X, Mller H, Loomes M, Comley R, Luo S (eds) Medical Imaging and Informatics, Lecture Notes in Computer Science, vol 4987. Springer, Berlin Heidelberg, pp 51–56. doi:10.1007/978-3-540-79490-5_8

  62. Zhao W, Chellappa R, Phillips PJ, Rosenfeld A (2003) Face recognition: a literature survey. ACM Comput Surv 35(4):399–458. doi:10.1145/954339.954342

    Article  Google Scholar 

  63. Zhou Q, Chen S, Liu J, Tang X (2011) Edge-preserving single image super-resolution. In: Proceedings of the 19th ACM international conference on multimedia, MM ’11. ACM, New York, pp 1037–1040 (2011). doi:10.1145/2072298.2071932

  64. Zou W, Yuen P (2010) Very low resolution face recognition problem. In: Fourth IEEE international conference on biometrics: theory applications and systems (BTAS), pp 1–6. doi:10.1109/BTAS.2010.5634490

  65. Zou W, Yuen P (2012) Very low resolution face recognition problem. IEEE Trans Image Process 21(1):327–340. doi:10.1109/TIP.2011.2162423

    Article  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Srimanta Mandal.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mandal, S., Thavalengal, S. & Sao, A.K. Explicit and implicit employment of edge-related information in super-resolving distant faces for recognition. Pattern Anal Applic 19, 867–884 (2016). https://doi.org/10.1007/s10044-015-0512-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10044-015-0512-0

Keywords

Navigation