A real-time attitude recognition by eye-tracking
Proceedings of the Third International Conference on Internet Multimedia Computing and Service
Emotion recognition using hidden Markov models from facial temperature sequence
ACII'11 Proceedings of the 4th international conference on Affective computing and intelligent interaction - Volume Part II
"FaceLight": potentials and drawbacks of thermal imaging to infer driver stress
Proceedings of the 4th International Conference on Automotive User Interfaces and Interactive Vehicular Applications
Are you really smiling at me? spontaneous versus posed enjoyment smiles
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part III
Eyeglasses removal of thermal image based on visible information
Information Fusion
Image and Vision Computing
Eye localization from infrared thermal images
MPRSS'12 Proceedings of the First international conference on Multimodal Pattern Recognition of Social Signals in Human-Computer-Interaction
Eye localization from thermal infrared images
Pattern Recognition
Image and Vision Computing
Sparse tensor embedding based multispectral face recognition
Neurocomputing
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To date, most facial expression analysis has been based on visible and posed expression databases. Visible images, however, are easily affected by illumination variations, while posed expressions differ in appearance and timing from natural ones. In this paper, we propose and establish a natural visible and infrared facial expression database, which contains both spontaneous and posed expressions of more than 100 subjects, recorded simultaneously by a visible and an infrared thermal camera, with illumination provided from three different directions. The posed database includes the apex expressional images with and without glasses. As an elementary assessment of the usability of our spontaneous database for expression recognition and emotion inference, we conduct visible facial expression recognition using four typical methods, including the eigenface approach [principle component analysis (PCA)], the fisherface approach [PCA + linear discriminant analysis (LDA)], the Active Appearance Model (AAM), and the AAM-based + LDA. We also use PCA and PCA+LDA to recognize expressions from infrared thermal images. In addition, we analyze the relationship between facial temperature and emotion through statistical analysis. Our database is available for research purposes.