Unsupervised emotion recognition algorithm based on improved deep belief model in combination with probabilistic linear discriminant analysis

2019 ◽  
Vol 23 (3-4) ◽  
pp. 553-562 ◽  
Author(s):  
Ying Xiao ◽  
Deyan Wang ◽  
Ligong Hou
Keyword(s):  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Emotion Recognition ◽  
Recognition Algorithm ◽  
Linear Discriminant ◽  
Belief Model

FUSING GLOBAL AND LOCAL COMPLETE LINEAR DISCRIMINANT FEATURES BY FUZZY INTEGRAL FOR FACE RECOGNITION

2008 ◽  
Vol 22 (07) ◽  
pp. 1427-1445 ◽  
Author(s):  
CHENGYUAN ZHANG ◽  
QIUQI RUAN ◽  
YI JIN
Keyword(s):  
Face Recognition ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Null Space ◽  
Information Aggregation ◽  
Recognition Algorithm ◽  
Fuzzy Integral ◽  
Fisher Discriminant Analysis ◽  
Linear Discriminant ◽  
Global And Local

Face recognition becomes very difficult in a complex environment, and the combination of multiple classifiers is a good solution to this problem. A novel face recognition algorithm GLCFDA-FI is proposed in this paper, which fuses the complementary information extracted by complete linear discriminant analysis from the global and local features of a face to improve the performance. The Choquet fuzzy integral is used as the fusing tool due to its suitable properties for information aggregation. Experiments are carried out on the CAS-PEAL-R1 database, the Harvard database and the FERET database to demonstrate the effectiveness of the proposed method. Results also indicate that the proposed method GLCFDA-FI outperforms five other commonly used algorithms — namely, Fisherfaces, null space-based linear discriminant analysis (NLDA), cascaded-LDA, kernel-Fisher discriminant analysis (KFDA), and null-space based KFDA (NKFDA).

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

A view-invariant gait recognition algorithm based on a joint-direct linear discriminant analysis

2017 ◽  
Author(s):  
Jose Portillo-Portillo ◽  
Roberto Leyva ◽  
Victor Sanchez ◽  
Gabriel Sanchez-Perez ◽  
Hector Perez-Meana ◽  
...  
Keyword(s):  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Gait Recognition ◽  
Recognition Algorithm ◽  
Linear Discriminant

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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