Journal
IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS
Volume 42, Issue 4, Pages 980-992Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TSMCB.2012.2192269
Keywords
Avatar reference; emotion avatar image (EAI); face registration; person-independent emotion recognition; Scale-invariant feature transform (SIFT) flow
Categories
Funding
- National Science Foundation [0727129, 0903667]
- Direct For Computer & Info Scie & Enginr
- Division of Computing and Communication Foundations [0727129] Funding Source: National Science Foundation
- Direct For Education and Human Resources [0903667] Funding Source: National Science Foundation
- Division Of Graduate Education [0903667] Funding Source: National Science Foundation
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Existing video-based facial expression recognition techniques analyze the geometry-based and appearance-based information in every frame as well as explore the temporal relation among frames. On the contrary, we present a new image-based representation and an associated reference image called the emotion avatar image (EAI), and the avatar reference, respectively. This representation leverages the out-of-plane head rotation. It is not only robust to outliers but also provides a method to aggregate dynamic information from expressions with various lengths. The approach to facial expression analysis consists of the following steps: 1) face detection; 2) face registration of video frames with the avatar reference to form the EAI representation; 3) computation of features from EAIs using both local binary patterns and local phase quantization; and 4) the classification of the feature as one of the emotion type by using a linear support vector machine classifier. Our system is tested on the Facial Expression Recognition and Analysis Challenge (FERA2011) data, i.e., the Geneva Multimodal Emotion Portrayal-Facial Expression Recognition and Analysis Challenge (GEMEP-FERA) data set. The experimental results demonstrate that the information captured in an EAI for a facial expression is a very strong cue for emotion inference. Moreover, our method suppresses the person-specific information for emotion and performs well on unseen data.
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