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Comparison of Facial Alignment Techniques: With Test Results on Gender Classification Task

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Video Analytics for Audience Measurement (VAAM 2014)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 8811))

Abstract

In this paper, different facial alignment techniques are revised in terms of their effects on machine learning algorithms. This paper, investigates techniques that are widely accepted in literature and measures their effect on gender classification task. There is no special reason on selecting gender classification task, any other task could have been chosen. In audience measurement systems, many important demographics, i.e. gender, age, facial expression, can be measured by using machine learning algorithms. Moreover; in such a system, these demographics are so substantial since their discriminative features on customers’ habits. Due to this importance, any performance enhancement on any machine learning algorithm becomes important. A carefully chosen alignment method can boost performance of machine learning algorithms used within system.

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References

  1. Tan, X., Triggs, B.: Enhanced local texture feature sets for face recognition under difficult lighting conditions. IEEE Trans. Image Process. 19(6), 1635–1650 (2010)

    Article  MathSciNet  Google Scholar 

  2. Viola, P.A., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Proceedings of IEEE Computer Society Conference on Computer Vision Pattern Recognition, vol. 1, pp. 511–518 (2001)

    Google Scholar 

  3. Zhang, L., Chu, R., Xiang, S., Liao, S., Li, S.Z.: Face detection based on multi-block LBP representation. In: Lee, S.-W., Li, S.Z. (eds.) ICB 2007. LNCS, vol. 4642, pp. 11–18. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  4. Cortes, C., Vladimir, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995)

    MATH  Google Scholar 

  5. Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)

    Article  MATH  Google Scholar 

  6. Freund, Y.: Boosting a weak learning algorithm by majority. Inf. Comput. 121(2), 256–285 (1995)

    Article  MathSciNet  MATH  Google Scholar 

  7. Huang, G.B., et al.: Labeled faces in the wild: A database for studying face recognition in unconstrained environments. Technical Report 07-49, University of Massachusetts, Amherst (2007)

    Google Scholar 

  8. Wolf, L., Hassner, T., Taigman, Y.: Effective face recognition by combining multiple descriptors and learned background statistics. IEEE Trans. Pattern Anal. Mach. Intell. (TPAMI) 33(10) (2011)

    Google Scholar 

  9. Huang, G.B., Mattar, M.A., Lee, H., Learned-Miller, E.G.: Learning to align from scratch. In: Bartlett, P.L., Pereira, F.C.N., Burges, C.J.C., Bottou, L., Weinberger, K.Q. (eds.) NIPS (2012)

    Google Scholar 

  10. Huang, G., Jain, V., Learned-Miller, E.: Unsupervised joint alignment of complex images. In: IEEE International Conference on Computer Vision (2007)

    Google Scholar 

  11. Jensen, F.V.: An Introduction to Bayesian Networks. Springer, New York (1996)

    Google Scholar 

  12. Lian, H.-C., Lu, B.-L.: Multi-view gender classification using local binary patterns and support vector machines. In: Wang, J., Yi, Z., Żurada, J.M., Lu, B.-L., Yin, H. (eds.) ISNN 2006. LNCS, vol. 3972, pp. 202–209. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  13. Jabid, T., Kabir, H., Chae, O.: Gender classification using local directional pattern (LDP). In: 2010 International Conference on Pattern Recognition (ICPR 2010), pp. 2162–2164 (2010)

    Google Scholar 

  14. Wang, J.G., Li, J., Yau, W.Y., Sung, E.: Boosting dense SIFT descriptors and shape contexts of face images for gender recognition. In: Proceedings of CVPR, San Francisco, pp. 96–102 (2010)

    Google Scholar 

  15. Bolme, D.S., Draper, B.A., Beveridge, J.R.: Average of synthetic exact filters. In: CVPR (2009)

    Google Scholar 

  16. Wang, P., Green, M., Ji, Q., Wayman, J.: Automatic eye detection and its validation. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 164–171 (2005)

    Google Scholar 

  17. Dantone, M., Gall, J., Fanelli, G., Van Gool, L.: Real-time facial feature detection using conditional regression forests. In: CVPR (2012)

    Google Scholar 

  18. Huang, D., Shan, C., Ardabilian, M., Wang, Y., Chen, L.: Local binary patterns and its application to facial image analysis: a survey. IEEE Trans. Syst. Man Cybern. Part C 41(6), 765–781 (2011)

    Article  Google Scholar 

  19. Ojala, T., Pietikäinen, M., Harwood, D.: A comparative study of texture measures with classification based on feature distributions. Pattern Recogn. 29(1), 51–59 (1996)

    Article  Google Scholar 

  20. Miller, E., Matsakis, N., Viola, P.: Learning from one example through shared densities on transforms. CVPR 1, 464–471 (2000)

    Google Scholar 

  21. Cootes, T.F., Taylor, C.J., Cooper, D.H., Graham, J.: Active shape models—their training and application. Comput. Vis. Image Underst. 61(1), 38–59 (1995)

    Article  Google Scholar 

  22. Cootes, T.F., Edwards, G.J., Taylor, C.J.: Active appearance models. IEEE TPAMI 23, 681–685 (2001)

    Article  Google Scholar 

  23. Cootes, T.F., Wheeler, G.V., Walker, K.N., Taylor, C.J.: View-based active appearance models. Image Vis. Comput. 20(9–10), 657–664 (2002)

    Article  Google Scholar 

  24. Shan, C.: Learning local binary patterns for gender classification on real-world face images. Pattern Recogn. Lett. 33(4), 431–437 (2012)

    Article  Google Scholar 

  25. Labeled Faces in the Wild-a (LFW-a). http://www.openu.ac.il/home/hassner/data/lfwa/

  26. Deep Funneled LFW. http://vis-www.cs.umass.edu/lfw/#deepfunnel-anchor

  27. Face Recognition Task on LFW. http://vis-www.cs.umass.edu/lfw/results.html

  28. Bradski, G.: The OpenCV Library. Dr. Dobb’s J. Softw. Tools (2000)

    Google Scholar 

  29. Delaunay Triangulation, http://en.wikipedia.org/wiki/Delaunay_triangulation

  30. Average male and female faces. http://www.uni-regensburg.de/

  31. Milborrow, S., Nicolls, F.: Active shape models with SIFT descriptors and MARS. VISAPP 1(2), 5 (2014)

    Google Scholar 

  32. Tanigman, Y., Yang. M, Ranzato, M., Wolf, L.: DeepFace: Closing the gap to human-level performance in face verification (2014)

    Google Scholar 

  33. Irfanoglu, M., Gokberk, B., Akarun, L.: 3d shape-based face recognition using automatically registered facial surfaces. In: Conference on Pattern Recognition, vol. 4, pp. 183–186 (2004)

    Google Scholar 

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Authors and Affiliations

  1. Adoniss Software Ltd., Middle East Technical University Technopolis, Ankara, Turkey

    Tunç Güven Kaya & Engin Firat

Authors
  1. Tunç Güven Kaya
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  2. Engin Firat
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Correspondence to Tunç Güven Kaya .

Editor information

Editors and Affiliations

  1. National Research Council of Italy, Arnesano, Lecce, Italy

    Cosimo Distante

  2. Dipartimento di Matematica e Informatica, University of Catania IT, Catania, Catania, Italy

    Sebastiano Battiato

  3. Queen Mary University of London, London, United Kingdom

    Andrea Cavallaro

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Kaya, T.G., Firat, E. (2014). Comparison of Facial Alignment Techniques: With Test Results on Gender Classification Task. In: Distante, C., Battiato, S., Cavallaro, A. (eds) Video Analytics for Audience Measurement. VAAM 2014. Lecture Notes in Computer Science(), vol 8811. Springer, Cham. https://doi.org/10.1007/978-3-319-12811-5_6

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  • DOI: https://doi.org/10.1007/978-3-319-12811-5_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-12810-8

  • Online ISBN: 978-3-319-12811-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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