An improved neural-network-based face detection and facial expression classification system

2005 ◽  
Author(s):  
I.-O. Stathopoulou ◽  
G.A. Tsihrintzis
Keyword(s):  
Neural Network ◽  
Facial Expression ◽  
Face Detection ◽  
Classification System ◽  
Facial Expression Classification ◽  
Expression Classification

A Design of Face Detection and Facial Expression Recognition Techniques Based on Boosting Schema

2011 ◽  
Vol 121-126 ◽  
pp. 617-621 ◽  
Author(s):  
Chang Yi Kao ◽  
Chin Shyurng Fahn
Keyword(s):  
Facial Expression ◽  
Face Detection ◽  
Facial Expression Recognition ◽  
Recognition Rate ◽  
Extraction Procedure ◽  
Expression Recognition ◽  
Classification Procedure ◽  
Facial Expression Classification ◽  
Human Faces ◽  
Expression Classification

During the development of the facial expression classification procedure, we evaluate three machine learning methods. We combine ABAs with CARTs, which selects weak classifiers and integrates them into a strong classifier automatically. We have presented a highly automatic facial expression recognition system in which a face detection procedure is first able to detect and locate human faces in image sequences acquired in real environments. We need not label or choose characteristic blocks in advance. In the face detection procedure, some geometrical properties are applied to eliminate the skin color regions that do not belong to human faces. In the facial feature extraction procedure, we only perform both the binarization and edge detection operations on the proper ranges of eyes, mouth, and eyebrows to obtain the 16 landmarks of a human face to further produce 16 characteristic distances which represent a kind of expressions. We realize a facial expression classification procedure by employing an ABA to recognize six kinds of expressions. The performance of the system is very satisfactory; whose recognition rate achieves more than 90%.

scholarly journals A Novel Based 3d Facial Expression Detection Using Recurrent Neural Network

2020 ◽  
pp. 48-53
Author(s):  
Jaswanth K S ◽  
D. Stalin David
Keyword(s):  
Neural Network ◽  
Facial Expression ◽  
Facial Expressions ◽  
Social Relations ◽  
Recurrent Neural Network ◽  
Breakdown Point ◽  
Early Age ◽  
Facial Expression Classification ◽  
Expression Classification ◽  
Highlight Extraction

People periodically have diverse facial expressions and disposition changes in this way. Human facial expression acknowledgment plays a really energetic part in social relations. The acknowledgment of feelings has been an dynamic breakdown point from early age. The real-time location of facial expressions like appall, upbeat, pitiful, irate, anxious, astonish. The proposed framework can recognize 6 diverse facial expression. A facial expression acknowledgment framework needs to perform location and change to 3D image, then the facial highlight extraction, and facial expression classification is worn. Out proposed strategy we should be utilizing Recurrent Neural Network (RNN). This RNN show is prepared on JAFEE and Yale database dataset. This framework has capacity to screen individuals’ feelings, to segregate between feelings and name them fittingly.

Facial Expression Classification Based on Ensemble Convolutional Neural Network

2020 ◽  
Vol 57 (14) ◽  
pp. 141501
Author(s):  
周涛 Zhou Tao ◽  
吕晓琪 Lü Xiaoqi ◽  
任国印 Ren Guoyin ◽  
谷宇 Gu Yu ◽  
张明 Zhang Ming ◽  
...  
Keyword(s):  
Neural Network ◽  
Facial Expression ◽  
Convolutional Neural Network ◽  
Facial Expression Classification ◽  
Expression Classification
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