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IJSTR >> Volume 9 - Issue 3, March 2020 Edition

International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616

Regression Model With Modified Linear Discriminant Analysis Features For Bimodal Emotion Recognition

[Full Text]



Gaikwad Kiran Pandhari, Manna Sheela Rani Chetty



Bimodal emotion recognition, MLDA, MFCC, Incomplete Sparse Least Square Regression, Feature Library



Now days recognizing the face accurately is becoming more challenging and essential task in the biometric authentication. Use of minimum facial features is important to reduce the complexity of designing the face recognition system. The performance of any emotion recognition system is mostly dependent on efficient design of face recognition system. Recently in the direction of emotion recognition a lot of the work is carried out. It is suggested by some researchers that use of only facial features or speech features are not sufficient to design emotion recognition system. Here in this paper the approach to extract the facial and speech features to recognize the emotion is proposed. Survey suggests that, combining both the features (facial features and speech features) to recognize the emotion improves the emotion state recognition accuracy. Proposed method uses extraction of facial features in both the directions (row and column) using maximum margin criteria with Modified Linear Discriminant Analysis (MLDA). The respective speech data signals are extracted using Mel-Frequency Cepstral Coefficients (MFCC) of the speech. The resultant features (facial features and speech features) are further integrated to construct an informative feature library. The constructed feature library provides a base for recognizing the emotions using advanced regression model, called as Incomplete Sparse Least Square Regression (ISLSR). After experimentation, the proposed approach is found to provide improved recognition accuracy of emotions than existing approaches.



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