Automatic Analysis of Facial Expressions: The State of the Art
IEEE Transactions on Pattern Analysis and Machine Intelligence
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
Coding Facial Expressions with Gabor Wavelets
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
Wavelet Energy Entropy as a New Feature Extractor for Face Recognition
ICIG '07 Proceedings of the Fourth International Conference on Image and Graphics
Facial feature extraction using complex dual-tree wavelet transform
Computer Vision and Image Understanding
Recognition of facial expressions using Gabor wavelets and learning vector quantization
Engineering Applications of Artificial Intelligence
Facial expression recognition based on Local Binary Patterns: A comprehensive study
Image and Vision Computing
GCIS '09 Proceedings of the 2009 WRI Global Congress on Intelligent Systems - Volume 02
Curvelet based face recognition via dimension reduction
Signal Processing
The curvelet transform for image denoising
IEEE Transactions on Image Processing
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This paper proposes the use of curvelet entropy for classifying facial expressions from still images. The idea behind this work is that the expressions impose non-rigid motions on the face thereby changing the orientations of facial curves occurring due to different types of expressions. Hence a multiresolution transform like curvelet which refines its domain by using orientation information may be applied for the task of expression classification. Since similarity of facial expressions has earlier been studied using Gabor wavelet which uses filters oriented in different directions on specific feature points in images, the orientation selectivity and information content of curvelet subbands at specific facial points are used here. The information at selected facial points are gathered using the entropy of the corresponding pixel at various subbands. The proposed method is evaluated in the JAFFE and Cohn-Kanade databases without and with cross-validations. Experimental results show that the curvelet subband entropy at selected points may be used to form effective features for classifying facial expressions.