Recognizing Human Facial Expressions From Long Image Sequences Using Optical Flow
IEEE Transactions on Pattern Analysis and Machine Intelligence
Coding, Analysis, Interpretation, and Recognition of Facial Expressions
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic Classification of Single Facial Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic Analysis of Facial Expressions: The State of the Art
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recognizing Action Units for Facial Expression Analysis
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Comprehensive Database for Facial Expression Analysis
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Pattern Classification (2nd Edition)
IEEE Transactions on Pattern Analysis and Machine Intelligence
Dynamics of Facial Expression Extracted Automatically from Video
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FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
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Facial action recognition for facial expression analysis from static face images
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Facial expression recognition from line-based caricatures
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Integrated Computer-Aided Engineering
On Decomposing an Unseen 3D Face into Neutral Face and Expression Deformations
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Facial expression biometrics using statistical shape models
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Image Communication
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Facial expression plays a key role in non-verbal face-to-face communication. It is a challenging task to develop an automatic facial expression reading and understanding system, especially, for recognizing the facial expression from a static image without any prior knowledge of the test subject. In this paper, we present a topographic modeling approach to recognize and analyze facial expression from single static images. The so-called topographic modeling is developed based on a novel facial expression descriptor, Topographic Context (TC), for representing and recognizing facial expressions. This proposed approach applies topographic analysis that treats the image as a 3D surface and labels each pixel by its terrain features. Topographic context captures the distribution of terrain labels in the expressive regions of a face. It characterizes the distinct facial expression while conserving abundant expression information and disregarding most individual characteristics. Experiments on person-dependent and person-independent facial expression recognition using two public databases (MMI and Cohn-Kanade database) show that TC is a good feature representation for recognizing basic prototypic expressions. Furthermore, we conduct the separability analysis of TC-based features by both a visualized dimensionality reduction example and a theoretical estimation using certain separability criterion. For an in-depth understanding of the recognition property of different expressions, the between-expression discriminability is also quantitatively evaluated using the separability criterion. Finally, we investigated the robustness of the extracted TC-based expression features in two aspects: the robustness to the distortion of detected face region and the robustness to different intensities of facial expressions. The experimental results show that our system achieved the best correct rate at 82.61% for the person-independent facial expression recognition.