Face Verification using LBP Feature and Clustering

Chenqi Wang, Kevin Lin, Yi-Ping Hung

2014

Abstract

In this paper, we present a mechanism to extract certain special faces—LBP-Faces, which are designed to represent different kinds of faces around the world, and utilize them as the basis to verify other faces. In particular, we show how our idea can integrate with Local Binary Pattern (LBP) and improve its performance. Other than most of the previous LBP-variant approaches, which, no matter try to improve coding mechanism or optimize the neighbourhood sizes, first divide a face into patch-level regions (e.g. 7×7 patches), concatenating histograms calculated in each patch to derive a rather long dimension vector, and then apply PCA to implement dimension reduction, our work use original LBP histograms, trying to retain the major properties such as discriminability and invariance, but in a much bigger component-level region (we divide faces into 7 components). In each component, we cluster LBP descriptors—in the form of histograms to derive N clustering centroids, which we define as LBP-Faces. Then, to any input face, we calculate its similarities with all these N LBP-Faces and use the similarities as final features to verify the face. It looks like we project the faces image into a new feature space—LBP-Faces space. The intuition within it is that when we depict an unknown face, we are prone to use description such as how likely the face’s eye or nose is to an known one. Result of our experiment on the Labeled Face in Wild (LFW) database shows that our method outperforms LBP in face verification.

References

  1. Ahonen, T., Hadid, A., and Pietikinen, M. (2004). Face recognition with local binary patterns. In Proc.7th European Conference on Computer Vision(ECCV), pages 469-481.
  2. Belhumeur, P., Hespanha, J., and Kriegman, D. (1997). Eigenfaces vs. fisherfaces: recognition using class specific linear projection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19(7):711- 720.
  3. Cao, Z., Yin, Q., Tang, X., and Sun, J. (2010). Face recognition with learning-based descriptor. In Proc. 23th IEEE Conference Computer Vision and Pattern Recognition (CVPR), pages 2707-2714.
  4. Cootes, T., Edwards, G., and Taylor, C. (2001). Active appearance models. IEEE Transactions on Pattern Analysis and Machine Intelligence,, 23(6):681-685.
  5. Huang, G. B., Mattar, M., Berg, T., and Learned-miller, E. (2007). E.: Labeled faces in the wild: A database for studying face recognition in unconstrained environments. Technical report.
  6. Liu, C. and Wechsler, H. (2002). Gabor feature based classification using the enhanced fisher linear discriminant model for face recognition. IEEE Transactions on Image Processing, 11(4):467-476.
  7. Nguyen, H. V. and Bai, L. (2011). Cosine similarity metric learning for face verification. In Proc. 10th Asian conference on Computer vision (ACCV), ACCV'10, pages 709-720, Berlin, Heidelberg. Springer-Verlag.
  8. Ojala, T., Pietikainen, M., and Maenpaa, T. (2002). Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(7):971-987.
  9. Rod, Z. P., Adams, R., and Bolouri, H. (2000). Dimensionality reduction of face images using discrete cosine transforms for recognition. In IEEE Conference on Computer Vision and Pattern Recognition.
  10. Sharma, G., Hussain, S., and Jurie, F. (2012). Local higherorder statistics (lhs) for texture categorization and facial analysis. In Proc.15th European Conference on Computer Vision (ECCV), pages 1-12. Springer Berlin Heidelberg.
  11. Tan, X. and Triggs, B. (2010). Enhanced local texture feature sets for face recognition under difficult lighting conditions. IEEE Transactions on Image Processing, 19(6):1635-1650.
  12. Turk, M. and Pentland, A. (1991). Face recognition using eigenfaces. In Computer Vision and Pattern Recognition, 1991. Proceedings CVPR 7891., IEEE Computer Society Conference on, pages 586-591.
  13. Verschae, R., Ruiz-Del-Solar, J., and Correa, M. (2008). Face Recognition in Unconstrained Environments: A Comparative Study. In Workshop on Faces in 'RealLife' Images: Detection, Alignment, and Recognition, Marseille, France. Erik Learned-Miller and Andras Ferencz and Frédéric Jurie.
  14. Viola, P. and Jones, M. (2001). Rapid object detection using a boosted cascade of simple features. In Proc. 14th IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), volume 1, pages I-511-I-518 vol.1.
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Paper Citation


in Harvard Style

Wang C., Lin K. and Hung Y. (2014). Face Verification using LBP Feature and Clustering . In Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2014) ISBN 978-989-758-003-1, pages 572-578. DOI: 10.5220/0004736905720578


in Bibtex Style

@conference{visapp14,
author={Chenqi Wang and Kevin Lin and Yi-Ping Hung},
title={Face Verification using LBP Feature and Clustering},
booktitle={Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2014)},
year={2014},
pages={572-578},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004736905720578},
isbn={978-989-758-003-1},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2014)
TI - Face Verification using LBP Feature and Clustering
SN - 978-989-758-003-1
AU - Wang C.
AU - Lin K.
AU - Hung Y.
PY - 2014
SP - 572
EP - 578
DO - 10.5220/0004736905720578