Expression recognition using fuzzy spatio-temporal modeling
Pattern Recognition
MM '09 Proceedings of the 17th ACM international conference on Multimedia
A New Canonical Correlation Analysis Algorithm with Local Discrimination
Neural Processing Letters
Super-resolution of human face image using canonical correlation analysis
Pattern Recognition
Artificial Intelligence Review
Facial expression recognition in JAFFE dataset based on Gaussian process classification
IEEE Transactions on Neural Networks
ICONIP'10 Proceedings of the 17th international conference on Neural information processing: models and applications - Volume Part II
Automatic facial expression recognition with AAM-Based feature extraction and SVM classifier
MICAI'06 Proceedings of the 5th Mexican international conference on Artificial Intelligence
Facial expression recognition using fuzzy kernel discriminant analysis
FSKD'06 Proceedings of the Third international conference on Fuzzy Systems and Knowledge Discovery
Facial expression recognition based on boostingtree
ISNN'06 Proceedings of the Third international conference on Advnaces in Neural Networks - Volume Part II
Using articulatory likelihoods in the recognition of dysarthric speech
Speech Communication
Facial expression recognition based on cortex-like mechanisms
ICSI'12 Proceedings of the Third international conference on Advances in Swarm Intelligence - Volume Part II
3D human face description: landmarks measures and geometrical features
Image and Vision Computing
Computational Intelligence and Neuroscience
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In this correspondence, we address the facial expression recognition problem using kernel canonical correlation analysis (KCCA). Following the method proposed by Lyons et al. and Zhang et al. , we manually locate 34 landmark points from each facial image and then convert these geometric points into a labeled graph (LG) vector using the Gabor wavelet transformation method to represent the facial features. On the other hand, for each training facial image, the semantic ratings describing the basic expressions are combined into a six-dimensional semantic expression vector. Learning the correlation between the LG vector and the semantic expression vector is performed by KCCA. According to this correlation, we estimate the associated semantic expression vector of a given test image and then perform the expression classification according to this estimated semantic expression vector. Moreover, we also propose an improved KCCA algorithm to tackle the singularity problem of the Gram matrix. The experimental results on the Japanese female facial expression database and the Ekman's "Pictures of Facial Affect" database illustrate the effectiveness of the proposed method.