A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Unsupervised texture segmentation using Gabor filters
Pattern Recognition
Recognizing Human Facial Expressions From Long Image Sequences Using Optical Flow
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic Interpretation and Coding of Face Images Using Flexible Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recognizing Action Units for Facial Expression Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Real-Time Face Detection
International Journal of Computer Vision
Facial Expression Recognition Based on Gabor Wavelet Transformation and Elastic Templates Matching
ICIG '04 Proceedings of the Third International Conference on Image and Graphics
Haar Features for FACS AU Recognition
FGR '06 Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition
Gabor wavelets and General Discriminant Analysis for face identification and verification
Image and Vision Computing
Gabor wavelets and General Discriminant Analysis for face identification and verification
Image and Vision Computing
Solving multiclass learning problems via error-correcting output codes
Journal of Artificial Intelligence Research
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This paper concentrates on the comparisons of systems that are used for the recognition of expressions generated by six upper face action units (AU s) by using Facial Action Coding System (FACS ). Haar wavelet, Haar-Like and Gabor wavelet coefficients are compared, using Adaboost for feature selection. The binary classification results by using Support Vector Machines (SVM ) for the upper face AU s have been observed to be better than the current results in the literature, for example 96.5% for AU2 and 97.6% for AU5 . In multi-class classification case, the Error Correcting Output Coding (ECOC ) has been applied. Although for a large number of classes, the results are not as accurate as the binary case, ECOC has the advantage of solving all problems simultaneously; and for large numbers of training samples and small number of classes, error rates are improved.