Emotion recognition in the wild via sparse transductive transfer linear discriminant analysis

2016 ◽  
Vol 10 (2) ◽  
pp. 163-172 ◽  
Author(s):  
Yuan Zong ◽  
Wenming Zheng ◽  
Xiaohua Huang ◽  
Keyu Yan ◽  
Jingwei Yan ◽  
...  
Keyword(s):  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Emotion Recognition ◽  
Linear Discriminant ◽  
In The Wild

Multi-scale Entropy and Multiclass Fisher’s Linear Discriminant for Emotion Recognition Based on Multimodal Signal

2020 ◽  
pp. 71-78
Author(s):  
Lutfi Hakim ◽  
Sepyan Purnama Kristanto ◽  
Alfi Zuhriya Khoirunnisaa ◽  
Adhi Dharma Wibawa
Keyword(s):  
Support Vector Machine ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Emotion Recognition ◽  
Pulse Rate ◽  
Dimensional Reduction ◽  
Physiological Signals ◽  
Support Vector ◽  
Linear Discriminant ◽  
Fisher’S Linear Discriminant

Emotion recognition using physiological signals has been a special topic frequently discussed by researchers and practitioners in the past decade. However, the use of SpO2 and Pulse rate signals for emotion recognitionisvery limited and the results still showed low accuracy. It is due to the low complexity of SpO2 and Pulse rate signals characteristics. Therefore, this study proposes a Multiscale Entropy and Multiclass Fisher’s Linear Discriminant Analysis for feature extraction and dimensional reduction of these physiological signals for improving emotion recognition accuracy in elders.  In this study, the dimensional reduction process was grouped into three experimental schemes, namely a dimensional reduction using only SpO2 signals, pulse rate signals, and multimodal signals (a combination feature vectors of SpO2 and Pulse rate signals). The three schemes were then classified into three emotion classes (happy, sad, and angry emotions) using Support Vector Machine and Linear Discriminant Analysis Methods. The results showed that Support Vector Machine with the third scheme achieved optimal performance with an accuracy score of 95.24%. This result showed a significant increase of more than 22%from the previous works.

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