JOURNAL BROWSE
Search
Advanced SearchSearch Tips
Face Image Analysis using Adaboost Learning and Non-Square Differential LBP
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
 Title & Authors
Face Image Analysis using Adaboost Learning and Non-Square Differential LBP
Lim, Kil-Taek; Won, Chulho;
  PDF(new window)
 Abstract
In this study, we presented a method for non-square Differential LBP operation that can well describe the micro pattern in the horizontal and vertical component. We proposed a way to represent a LBP operation with various direction components as well as the diagonal component. In order to verify the validity of the proposed operation, Differential LBP was investigated with respect to accuracy, sensitivity, and specificity for the classification of facial expression. In accuracy comparison proposed LBP operation obtains better results than Square LBP and LBP-CS operations. Also, Proposed Differential LBP gets better results than previous two methods in the sensitivity and specificity indicators `Neutral`, `Happiness`, `Surprise`, and `Anger` and excellence Differential LBP was confirmed.
 Keywords
Local Binary Pattern;Facial Expression;Adaboost Learning;
 Language
Korean
 Cited by
 References
1.
T.F. Cootes, G.J. Edwards, and C.J. Taylor, “Active Appearance Models,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 23, No. 6, pp. 681-685, 2001. crossref(new window)

2.
Y. Cheon and D. Kim, “A Natural Facial Expression Recognition Using Differential-AAM and k-NNS,” Journal of Pattern Recognition, Vol. 42, No. 7, pp. 1340-1350, 2008. crossref(new window)

3.
A.B. Ashraf, K. Prkachin, T. Chen, S. Lucey, P. Solomon, Z. Ambadar, et al., “The Painful Face-Pain Expression Recognition Using Active Appearance Models,” Journal of Image and Vision Computing, Vol. 12, No. 3, pp. 1788-1796, 2009. crossref(new window)

4.
N.D. Matthew, W. Garrison, P. Curtis, and A. Ralph, “EMPATH: A Neural Network that Categorizes Facial Expressions,” Journal of Cognitive Neuroscience, Vol. 14, No. 8, pp. 1158-1173, 2002. crossref(new window)

5.
I. Kotsia, I. Buciu, and I. Pitas, “An Analysis of Facial Expression Recognition under Partial Facial Image Occlusion,” Journal of Image and Vision Computing, Vol. 26, No. 7, pp. 1052-1067, 2008. crossref(new window)

6.
T. Ojala, M. Pietikäinen, and T. Mäenpää, “Multi-resolution Gray-scale and Rotation Invariant Texture Classification with Local Binary Patterns,” IEEE Transaction on Pattern Analysis and Machine Intelligence, Vol. 24, No. 7, pp. 971-987, 2002. crossref(new window)

7.
C. Shan, S. Gong, and P.W. McOwan, “Facial Expression Recognition based on “Local Binary Patterns: A Comprehensive Study,” Journal of Image and Vision Computing, Vol. 27, No. 6, pp. 803-816, 2009. crossref(new window)

8.
C. Won, “Recognition of Facial Emotion Using Multi-scale LBP,” Journal of Korea Multimedia Society, Vol. 17, No. 12, pp. 1383-1392, 2014. crossref(new window)

9.
S. Junding, Z. Shisong, and W Xiaosheng, “Image Retrieval based on an Improved CS-LBP Descriptor,” Proceeding of The 2nd IEEE International Conference on Information Management and Engineering, pp. 115-117, 2010.

10.
Y. Freund and R.E. Schapire. “A Short Introduction to Boosting,” Journal of Japanese Society for Artificial Intelligence, Vol. 14, No. 5, pp. 771-780, 1999.

11.
C. Cortes and V. Vapnik, “Support-Vector Networks,” Journal of Machine Learning, Vol. 20, No. 3, pp. 273-297, 1995.

12.
C.W. Hsu and C.J. Lin, “A Comparison of Methods for Mu1ticlass Support Vector Machines,” IEEE Transactions on Neural Networks, Vol. 13, No. 2, pp. 415-425, 2002. crossref(new window)