Skip to main content
SpringerLink
Log in

Fully automatic face recognition framework based on local and global features

  • Original Paper
  • Published:
Machine Vision and Applications Aims and scope Submit manuscript

Abstract

Face recognition algorithms can be divided into two categories: holistic and local feature-based approaches. Holistic methods are very popular in recent years due to their good performance and high efficiency. However, they depend on careful positioning of the face images into the same canonical pose, which is not an easy task. On the contrary, some local feature-based approaches can achieve good recognition performances without additional alignment. But their computational burden is much heavier than holistic approaches. To solve these problems in holistic and local feature-based approaches, we propose a fully automatic face recognition framework based on both the local and global features. In this work, we propose to align the input face images using multi-scale local features for the holistic approach, which serves as a filter to narrow down the database for further fine matching. The computationally heavy local feature-based approach is then applied on the narrowed database. This fully automatic framework not only speeds up the local feature-based approach, but also improves the recognition accuracy comparing with the holistic and local approaches as shown in the experiments.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Ahonen T., Hadid A., Pietikäinen M.: Face description with local binary patterns: application to face recognition. IEEE Trans. Pattern Anal. Mach. Intell. 28, 2037–2041 (2006)

    Article  Google Scholar 

  2. Belhumeur P., Hespanha J., Kriegman D.: Eigenfaces vs. fisherfaces: recognition using class specific linear projection. IEEE Trans. Pattern Anal. Mach. Intell. 19(7), 711–720 (1997)

    Article  Google Scholar 

  3. Berg, T., Berg, A., Maire, M., White, R., Teh, Y., Learned-Miller, E., Forsyth, D.: Names and faces in the news. In: CVPR (2004)

  4. Bicego, M., Lagorio, A., Grosso, E., Tistarelli, M.: On the use of sift features for face authentication. In: Workshop on Computer Vision and Pattern Recog, pp. 35–40 (2006)

  5. Brown M., Hua G., Winder S.: Discriminative learning of local image descriptors. IEEE Trans. Pattern Anal. Mach. Intell. 33(1), 43–57 (2011)

    Article  Google Scholar 

  6. Cevikalp H., Neamtu M., Wilkes M., Barkana A.: Discriminative common vectors for face recognition. IEEE Trans. Pattern Anal. Mach. Intell. 27(1), 4–13 (2005)

    Article  Google Scholar 

  7. Chen, Y., Kubo, K.: A robust eye detection and tracking technique using gabor filters. In: IEEE International Conference on Intelligent Information Hiding and Multimedia Signal Processing, vol. 1, pp. 109–112 (2007)

  8. Choi J., Neve W., Plataniotis K., Ro Y.: Collaborative face recognition for improved face annotation in personal photo collections shared on online social networks. IEEE Trans. Multimed. 13(1), 14–28 (2011)

    Article  Google Scholar 

  9. Cootes, T., Edwards, G., Taylor, C.: Active apprearnce models. In: ECCV (1998)

  10. Cruz, C., Sucar, L., Morales, E.: Real-time face recognition for human-robot interaction. In: IEEE International Conference on Automatic Face and Gesture Recognition, pp. 1–6 (2008)

  11. D’Orazio T., Leo M., Cicirelli G., Distante A.: An algorithm for real time eye detection in face images. ICPR 3, 278–281 (2004)

    Google Scholar 

  12. Everingham, M., Sivic, J., Zisserman, A.: “Hello! My name is... Buffy”—automatic naming of characters in TV video. In: BMVC, pp. 889–908 (2006)

  13. Fan R., Chang K., Hsieh C., Wang X., Lin C.: Liblinear: a library for large linear classification. JMLR 9, 1871–1874 (2008)

    MATH  Google Scholar 

  14. Fernandez, C., Vicente, M.: Face recognition using multiple interest point detectors and sift descriptors. In: IEEE International Conference on Automatic Face and Gesture Recognition, pp. 1–7 (2008)

  15. Geng, C., Jiang, X.: Face recognition using sift features. In: ICIP, pp. 3313–3316 (2009)

  16. Geng, C., Jiang, X.: Sift features for face recognition. In: IEEE International Conference on Computer Science and Information Technology, pp. 598–602 (2009)

  17. Geng C., Jiang X.: Face recognition based on the multi-scale local image structures. Pattern Recognit. 44(10–11), 2565–2575 (2011)

    Article  Google Scholar 

  18. Hadid, A., Heikkila, J., Silver, O., Pietikäinen, M.: Face and eye detection for person suthentication in mobile phones. In: IEEE International Conference on Distributed Smart Camera, pp. 101–108 (2007)

  19. Hastie T., Tibshirani R., Friedman J.: The elements of statistical learning. Springer, Berlin (2009)

    Book  MATH  Google Scholar 

  20. Hua, G., Brown, M., Winder, S.: Discriminant embedding for local image descriptors. In: ICCV, pp. 1–8 (2007)

  21. Huang, G., Jain, V., Learned-Miller, E.: Unsupervised joint alignment of complex images. In: ICCV, pp. 1–8 (2007)

  22. Jiang X.D.: Asymmetric principal component and discriminant analyses for pattern classification. IEEE Trans. Pattern Anal. Mach. Intell. 31(5), 931–937 (2009)

    Article  Google Scholar 

  23. Jiang X.D.: Linear subspace learning-based dimensionality reduction. Signal Process. Mag. 28(2), 16–26 (2011)

    Article  Google Scholar 

  24. Jiang X.D., Mandal B., Kot A.: Eigenfeature regularization and extraction in face recognition. IEEE Trans. Pattern Anal. Mach. Intell. 30(3), 383–394 (2008)

    Article  Google Scholar 

  25. Jung, S., Chung, Y., Yoo, J., Moon, K.: Real-time face verification for mobile platforms. Advances in visual computing pp. 823–832 (2008)

  26. Kirby M., Sirovich L.: Application of karhunen–loeve procedure for the characterization of human faces. IEEE Trans. Pattern Anal. Mach. Intell. 12(1), 103–108 (1990)

    Article  Google Scholar 

  27. Kisku, D., Tistarelli, M., Sing, J., Gupta, P.: Face recognition by fusion of local and global matching scores using ds theory: an evaluation with uni-classifier and multi-classifier paradigm. In: IEEE Computer Vision and Pattern Recognition, pp. 60–65 (2009)

  28. Kumar, N.: Attribute face service. http://afs.automaticfacesystems.com/

  29. Kumar N., Berg A., Belhumeur P., Nayar S.: Describable visual attributes for face verification and image search. IEEE Trans. Pattern Anal. Mach. Intell. 33(10), 1962–1977 (2011)

    Article  Google Scholar 

  30. Li, G., Cai, X., Li, X., Liu, Y.: An efficient face normalization algorithm based on eye detection. In: IEEE International Conference on Intelligent Robots and Systems, pp. 3843–3848 (2006)

  31. Li, S., Yan, S., Zhang, H., Cheng, Q.: Multi-view face alignment using direct appearnce models. In: IEEE International Conference on Automatic Face and Gesture Recognition (2002)

  32. Lin, D., Yang, C.: Real-time eye detection using face circle fitting and dark-pixel filtering. In: ICME, pp. 1167–1170 (2004)

  33. Lindeberg T.: Feature detection with automatic scale selection. IJCV 30, 79–116 (1998)

    Article  Google Scholar 

  34. Lowe D.: Object recognition from local scale-invariant features. ICCV 2, 1150–1157 (1999)

    Google Scholar 

  35. Lowe D.: Distinctive image features from scale-invariant keypoints. IJCV 60, 91–110 (2004)

    Article  Google Scholar 

  36. Luo, J., Ma, Y., Takikawa, E., Lao, S., Kawade, M., Lu, B.: Person-specific sift features for face recognition. In: IEEE International Conference on Acoustics, Speech and Signal Processing, vol. 2, pp. 593–596 (2007)

  37. Martinez, A., Benavente, R.: Cvc technical report #24. Technical report (1998)

  38. Nefian, A.: Embedded bayesian networks for face recognition. In: ICME, pp. 133–136 (2002)

  39. Park, C., Kwak, J., Park, H., Moon, Y.: An effective method for eye detection based on texture information. In: IEEE International Conference on on Information Technology Convergence, pp. 586–589 (2007)

  40. Ren, J., Jiang, X.: Eye detection based on rank order filter. In: IEEE International Conference on Information, Communications and Signal Processing, pp. 1–4 (2009)

  41. Ren, J., Jiang, X.: Fast eye localization based on pixel differences. In: ICIP, pp. 2733–2736 (2009)

  42. Ren, J., Jiang, X., Yuan, J.: A fast and accurate cascade subspace face/eye detector on mobile devices. In: International Conference on Computer Vision (ICCV), Workshop on Mobile Vision, pp. 84–91 (2011)

  43. Riopka, T., Boult, T.: The eyes have it. In: ACM SIGMM Workshop on Biometrics Methods and Applications, pp. 9–16 (2003)

  44. Rosenberger, C., Brun, L.: Similarity-based matching for face authentication. In: ICPR, pp. 1–4 (2008)

  45. Ross A., Jain A.: Information fusion in biometrics. Pattern Recogn. Lett. 24(13), 2115–2125 (2003)

    Article  Google Scholar 

  46. Samaria, F., Harter, A.: Parameterisation of a stochastic model for human face identification. In: 2nd IEEE Workshop on Applications of Computer Vision, pp. 138–142 (1994)

  47. Wang X., Tang X.: A unified framework for subspace face recognition. IEEE Trans. Pattern Anal. Mach. Intell. 26(9), 1222–1228 (2004)

    Article  MathSciNet  Google Scholar 

  48. Wu, J., Trivedi, M.: A binary tree for probability learning in eye detection. In: Workshop on Computer Vision and Pattern Recognition, pp. 170–177 (2005)

  49. Yan S., Xu D., Zhang B., Yang Q., Zhang H., Lin S.: Graph embedding and extensions: a general framework for dimensionality reduction. IEEE Trans. Pattern Anal. Mach. Intell. 29(1), 40–51 (2007)

    Article  Google Scholar 

  50. Yang J., Frangi A., Yang J., Zhang D., Jin Z.: KPCA plus LDA: a complete kernel fisher discriminant framework for feature extraction and recognition. IEEE Trans. Pattern Anal. Mach. Intell. 27(2), 230–244 (2005)

    Article  Google Scholar 

  51. Ye J., Janardan R., Park C., Park H.: An optimization criterion for generalized discriminant analysis on undersampled problems. IEEE Trans. Pattern Anal. Mach. Intell. 26(8), 982–994 (2004)

    Article  Google Scholar 

  52. Zheng W., Tang X.: Fast algorithm for updating the discriminant vectors of dual-space lda. IEEE Trans. Info. Forensics Secur. 4(3), 418–427 (2009)

    Article  MathSciNet  Google Scholar 

  53. Zhou, Y., Gu, L., Zhang, H.: Bayesian tangent shape model: Estimating shape and pose parameters via bayesian inference. In: CVPR (2003)

  54. Zhou, Y., Zhang, W., Tang, X., Shum, H.: A bayesian mixture model for multi-view face alignment. In: CVPR (2005)

Download references

Author information

Authors and Affiliations

  1. School of Electrical and Electronic Engineering, Nanyang Technological University, Nanyang Link, Singapore, 639798, Singapore

    Cong Geng & Xudong Jiang

Authors
  1. Cong Geng
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xudong Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Cong Geng.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Geng, C., Jiang, X. Fully automatic face recognition framework based on local and global features. Machine Vision and Applications 24, 537–549 (2013). https://doi.org/10.1007/s00138-012-0423-7

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00138-012-0423-7

Keywords

Search

Navigation