DocumentCode :
33918
Title :
Speaking Effect Removal on Emotion Recognition From Facial Expressions Based on Eigenface Conversion
Author :
Chung-Hsien Wu ; Wen-Li Wei ; Jen-Chun Lin ; Wei-Yu Lee
Author_Institution :
Dept. of Comput. Sci. & Inf. Eng., Nat. Cheng Kung Univ., Tainan, Taiwan
Volume :
15
Issue :
8
fYear :
2013
fDate :
Dec. 2013
Firstpage :
1732
Lastpage :
1744
Abstract :
Speaking effect is a crucial issue that may dramatically degrade performance in emotion recognition from facial expressions. To manage this problem, an eigenface conversion-based approach is proposed to remove speaking effect on facial expressions for improving accuracy of emotion recognition. In the proposed approach, a context-dependent linear conversion function modeled by a statistical Gaussian Mixture Model (GMM) is constructed with parallel data from speaking and non-speaking facial expressions with emotions. To model the speaking effect in more detail, the conversion functions are categorized using a decision tree considering the visual temporal context of the Articulatory Attribute (AA) classes of the corresponding input speech segments. For verification of the identified quadrant of emotional expression on the Arousal-Valence (A-V) emotion plane, which is commonly used to dimensionally define the emotion classes, from the reconstructed facial feature points, an expression template is constructed to represent the feature points of the non-speaking facial expressions for each quadrant. With the verified quadrant, a regression scheme is further employed to estimate the A-V values of the facial expression as a precise point in the A-V emotion plane. Experimental results show that the proposed method outperforms current approaches and demonstrates that removing the speaking effect on facial expression is useful for improving the performance of emotion recognition.
Keywords :
Gaussian processes; decision trees; eigenvalues and eigenfunctions; emotion recognition; face recognition; regression analysis; GMM; arousal-valence emotion plane; articulatory attribute classes; context-dependent linear conversion function; decision tree; eigenface conversion; emotion recognition; expression template; facial expression; facial feature points; regression scheme; speaking effect removal; statistical Gaussian mixture model; visual temporal context; Active appearance model; Context modeling; Emotion recognition; Face recognition; Facial features; Speech; Visualization; Arousal-valence emotion plane; articulatory attribute; conversion function; emotion recognition; facial expression;
fLanguage :
English
Journal_Title :
Multimedia, IEEE Transactions on
Publisher :
ieee
ISSN :
1520-9210
Type :
jour
DOI :
10.1109/TMM.2013.2272917
Filename :
6557471
Link To Document :
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