Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Affective computing
Coding Facial Expressions with Gabor Wavelets
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
Comprehensive Database for Facial Expression Analysis
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
The CMU Pose, Illumination, and Expression (PIE) Database
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Robust Real-Time Face Detection
International Journal of Computer Vision
Face Description with Local Binary Patterns: Application to Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Facial expression recognition based on Local Binary Patterns: A comprehensive study
Image and Vision Computing
JustClick: personalized image recommendation via exploratory search from large-scale Flickr images
IEEE Transactions on Circuits and Systems for Video Technology
Dynamics of facial expression extracted automatically from video
Image and Vision Computing
An interactive approach for filtering out junk images from keyword-based google search results
IEEE Transactions on Circuits and Systems for Video Technology
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Facial expression recognition plays an important role in web images understanding. Since current databases for expression learning are usually achieved in experimental environment which are different from actual emotions, and the samples in each database is small, it is hard to train good expression classifiers with them, and which leads to a low expression classification ability, especially when recognizing expressions in web images. In this paper, a novel facial expression database construction method is suggested: First, a large-scale social label images are obtained by the Google web search with the keywords of happiness, sadness, surprise, anger, disgust and fear respectively; Then, unrelated images are filtered as junk images interactively by the hyperbolic visualization technique and the expression database is constructed. All the images in the database are from real social network, so the database is more proper to train classifiers for recognizing expressions in web images..