Multi-task Cascaded and Densely Connected Convolutional Networks Applied to Human Face Detection and Facial Expression Recognition System

2019 ◽  
Author(s):  
Kuan-Yu Chou ◽  
Yi-Wen Cheng ◽  
Wei-Ren Chen ◽  
Yon-Ping Chen
Keyword(s):  
Facial Expression ◽  
Face Detection ◽  
Facial Expression Recognition ◽  
Recognition System ◽  
Expression Recognition ◽  
Human Face ◽  
Convolutional Networks ◽  
Human Face Detection

A Face Based Real Time Communication for Physically and Speech Disabled People

2013 ◽  
pp. 1434-1460
Author(s):  
Ong Chin Ann ◽  
Marlene Valerie Lu ◽  
Lau Bee Theng
Keyword(s):  
Facial Expression ◽  
Real Time ◽  
Face Detection ◽  
Facial Expression Recognition ◽  
Recognition System ◽  
Detection Algorithm ◽  
Expression Recognition ◽  
Time Behaviour ◽  
The Disabled ◽  
Detection Stage

The main purpose of this research is to enhance the communication of the disabled community. The authors of this chapter propose an enhanced interpersonal-human interaction for people with special needs, especially those with physical and communication disabilities. The proposed model comprises of automated real time behaviour monitoring, designed and implemented with the ubiquitous and affordable concept in mind to suit the underprivileged. In this chapter, the authors present the prototype which encapsulates an automated facial expression recognition system for monitoring the disabled, equipped with a feature to send Short Messaging System (SMS) for notification purposes. The authors adapted the Viola-Jones face detection algorithm at the face detection stage and implemented template matching technique for the expression classification and recognition stage. They tested their model with a few users and achieved satisfactory results. The enhanced real time behaviour monitoring system is an assistive tool to improve the quality of life for the disabled by assisting them anytime and anywhere when needed. They can do their own tasks more independently without constantly being monitored physically or accompanied by their care takers, teachers, or even parents. The rest of this chapter is organized as follows. The background of the facial expression recognition system is reviewed in Section 2. Section 3 is the description and explanations of the conceptual model of facial expression recognition. Evaluation of the proposed system is in Section 4. Results and findings on the testing are laid out in Section 5, and the final section concludes the chapter.

Human Emotion Recognition Based on Face and Facial Expression Detection Using Deep Belief Network Under Complicated Backgrounds

2020 ◽  
Vol 34 (14) ◽  
pp. 2056010
Author(s):  
Lei Huang ◽  
Fei Xie ◽  
Jing Zhao ◽  
Shibin Shen ◽  
Weiran Guang ◽  
...  
Keyword(s):  
Facial Expression ◽  
Emotion Recognition ◽  
Face Detection ◽  
Skin Color ◽  
Facial Expression Recognition ◽  
Expression Recognition ◽  
Human Face ◽  
Human Emotion ◽  
The Face ◽  
Color Enhancement

The human emotion recognition based on facial expression has a significant meaning in the application of intelligent man–machine interaction. However, the human face images vary largely in real environments due to the complex backgrounds and luminance. To solve this problem, this paper proposes a robust face detection method based on skin color enhancement model and a facial expression recognition algorithm with block principal component analysis (PCA). First, the luminance range of human face image is broadened and the contrast ratio of skin color is strengthened by the homomorphic filter. Second, the skin color enhancement model is established using YCbCr color space components to locate the face area. Third, the feature based on differential horizontal integral projection is extracted from the face. Finally, the block PCA with deep neural network is used to accomplish the facial expression recognition. The experimental results indicate that in the case of weaker illumination and more complicated backgrounds, both the face detection and facial expression recognition can be achieved effectively by the proposed algorithm, meanwhile the mean recognition rate obtained by the facial expression recognition method is improved by 2.7% comparing with the traditional Local Binary Patterns (LBPs) method.

A Face Based Real Time Communication for Physically and Speech Disabled People

2011 ◽  
pp. 70-102
Author(s):  
Ong Chin Ann ◽  
Marlene Valerie Lu ◽  
Lau Bee Theng
Keyword(s):  
Facial Expression ◽  
Real Time ◽  
Face Detection ◽  
Facial Expression Recognition ◽  
Recognition System ◽  
Detection Algorithm ◽  
Expression Recognition ◽  
Time Behaviour ◽  
The Disabled ◽  
Detection Stage

The main purpose of this research is to enhance the communication of the disabled community. The authors of this chapter propose an enhanced interpersonal-human interaction for people with special needs, especially those with physical and communication disabilities. The proposed model comprises of automated real time behaviour monitoring, designed and implemented with the ubiquitous and affordable concept in mind to suit the underprivileged. In this chapter, the authors present the prototype which encapsulates an automated facial expression recognition system for monitoring the disabled, equipped with a feature to send Short Messaging System (SMS) for notification purposes. The authors adapted the Viola-Jones face detection algorithm at the face detection stage and implemented template matching technique for the expression classification and recognition stage. They tested their model with a few users and achieved satisfactory results. The enhanced real time behaviour monitoring system is an assistive tool to improve the quality of life for the disabled by assisting them anytime and anywhere when needed. They can do their own tasks more independently without constantly being monitored physically or accompanied by their care takers, teachers, or even parents. The rest of this chapter is organized as follows. The background of the facial expression recognition system is reviewed in Section 2. Section 3 is the description and explanations of the conceptual model of facial expression recognition. Evaluation of the proposed system is in Section 4. Results and findings on the testing are laid out in Section 5, and the final section concludes the chapter.

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