Advertisement

Feature Fusion for Facial Landmark Point Location

  • Gang ZhangEmail author
  • Jiansheng Chen
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 16)

Abstract

Size of regions and discrimination of features are important to local approaches for facial landmark point location. After size of regions is determined, importance of features is obvious. Three features are considered in the paper, i.e. scale invariant feature transform (SIFT) features, local binary pattern (LBP) features, and Gabor wavelet features. Three logistic regressors are trained by using them respectively, and then they are used to testify the effect of these features on classification performance. On this basis, three fusion features are considered, i.e. SIFT features and Gabor wavelet features, SIFT features and LBP features, and Gabor wavelet features and LBP features. Three logistic regressors are trained by using them, respectively. They will be used to testify the effect of these fusion features on classification performance compared with single features. It can be noted from experiments that fusion features are more discriminative than single features, and that all of the features in the fusion features cooperate at classification stage, instead of single features.

Keywords

Face alignment Landmark point location Fusion of features 

References

  1. 1.
    Saragih, J.M., Lucey, S., Cohn, J.F.: Deformable model fitting by regularized landmark mean-shift. Int. J. Comput. Vis. 91, 200–215 (2011)MathSciNetCrossRefzbMATHGoogle Scholar
  2. 2.
    Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60, 91–110 (2004)CrossRefGoogle Scholar
  3. 3.
    Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. 24, 971–987 (2002)CrossRefzbMATHGoogle Scholar
  4. 4.
    Arivazhagan, S., Ganesan, L., Priyal, S.P.: Texture classification using Gabor wavelets based rotation invariant features. Pattern Recogn. Lett. 27, 1976–1982 (2006)CrossRefGoogle Scholar
  5. 5.
    Lindeberg, T.: Scale-space theory: a basic tool for analysing structures at different scales. J. Appl. Stat. 21, 224–270 (1994)CrossRefGoogle Scholar
  6. 6.
    Mikolajczyk, K., Schmid, C.: An affine invariant interest point detector. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) Computer Vision, vol. 2350, pp. 128–142. Springer, Berlin (2002)Google Scholar
  7. 7.
    Zhang, D.S., Wong, A., Indrawan, M., Lu, G.: Content based image retrieval using Gabor texture features. In: Machine Learning and Cybernetics, vol. 2, pp. 392–395. IEEE, Qingdao (2000)Google Scholar
  8. 8.
    Jesorsky, O., Kirchberg, K.J., Frischholz, R.W.: Robust face detection using the hausdorff distance. In: Bigun, J., Smeraldi, F. (eds.) Audio- and Video-Based Biometric Person Authentication, vol. 2091, pp. 90–95. Springer, Berlin (2001)CrossRefGoogle Scholar
  9. 9.
    Wang, Y., Lucey, S., Cohn, J.F.: Enforcing convexity for improved alignment with constrained local models. In: Computer Vision and Pattern Recognition, pp. 1–8. IEEE Computer Society, Piscataway (2008)Google Scholar
  10. 10.
    Huang, G.B., Ramesh, M., Berg, T., Learned-Miller, E.: Labeled faces in the wild: a database for studying face recognition in unconstrained environments, Technical report. University of Massachusetts, Amherst (2007)Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.School of SoftwareShenyang University of TechnologyShenyangChina
  2. 2.Department of Electronic EngineeringTsinghua UniversityBeijingChina

Personalised recommendations