Facial Expression Recognition Based on MB-LGBP Feature and Multi-level Classification

2011 ◽  
pp. 37-42 ◽  
Author(s):  
Zheng Zhang ◽  
Chao Xu ◽  
Jiaxin Wang ◽  
Xiangning Chen
Keyword(s):  
Facial Expression ◽  
Facial Expression Recognition ◽  
Expression Recognition ◽  
Multi Level

Facial Emotion Recognition Using an Ensemble of Multi-Level Convolutional Neural Networks

2019 ◽  
Vol 33 (11) ◽  
pp. 1940015 ◽  
Author(s):  
Hai-Duong Nguyen ◽  
Soonja Yeom ◽  
Guee-Sang Lee ◽  
Hyung-Jeong Yang ◽  
In-Seop Na ◽  
...  
Keyword(s):  
Facial Expression ◽  
Emotion Recognition ◽  
Facial Expression Recognition ◽  
Research Area ◽  
Expression Recognition ◽  
Human Machine Interaction ◽  
Network Connections ◽  
Multi Level ◽  
Machine Interaction ◽  
Made In

Emotion recognition plays an indispensable role in human–machine interaction system. The process includes finding interesting facial regions in images and classifying them into one of seven classes: angry, disgust, fear, happy, neutral, sad, and surprise. Although many breakthroughs have been made in image classification, especially in facial expression recognition, this research area is still challenging in terms of wild sampling environment. In this paper, we used multi-level features in a convolutional neural network for facial expression recognition. Based on our observations, we introduced various network connections to improve the classification task. By combining the proposed network connections, our method achieved competitive results compared to state-of-the-art methods on the FER2013 dataset.

Cascaded Multi-level Transformed Dirichlet Process for Multi-pose Facial Expression Recognition

2018 ◽  
Vol 61 (11) ◽  
pp. 1605-1619 ◽  
Author(s):  
Qirong Mao ◽  
Feifei Zhang ◽  
Liangjun Wang ◽  
Sidian Luo ◽  
Ming Dong
Keyword(s):  
Facial Expression ◽  
Dirichlet Process ◽  
Facial Expression Recognition ◽  
Expression Recognition ◽  
Multi Level

scholarly journals Nonverbal Communication through Facial Expression in Diverse Conditions

2021 ◽  
Author(s):  
Mahesh Goyani
Keyword(s):  
Facial Expression ◽  
Template Matching ◽  
Facial Expression Recognition ◽  
Recognition Rate ◽  
Training Sample ◽  
Expression Recognition ◽  
Feature Based ◽  
Multi Level ◽  
Space Recognition ◽  
Vision Technique

In this chapter, we investigated computer vision technique for facial expression recognition, which increase both - the recognition rate and computational efficiency. Local and global appearance-based features are combined in order to incorporate precise local texture and global shapes. We proposed Multi-Level Haar (MLH) feature based system, which is simple and fast in computation. The driving factors behind using the Haar were its two interesting properties - signal compression and energy preservation. To depict the importance of facial geometry, we first segmented the facial components like eyebrows, eye, and mouth, and then applied feature extraction on these facial components only. Experiments are conducted on three well known publicly available expression datasets CK, JAFFE, TFEID and in-house WESFED dataset. The performance is measured against various template matching and machine learning classifiers. We achieved highest recognition rate for proposed operator with Discriminant Analysis Classifier. We studied the performance of proposed approach in several scenarios like expression recognition from low resolution, recognition from small training sample space, recognition in the presence of noise and so forth.

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