Title
Expression Recognition Algorithm Based On The Relative Relationship Of The Facial Landmarks
Abstract
In order to improve the facial expression recognition accuracy in complex contexts and decrease the identification time, this paper presents an efficient expression recognition algorithm based on feature points of the facial landmarks. In practice, facial expressions mainly focus on a few parts of muscle activity, which provides valuable reference to infer human emotions and intentions. Facial expression is a powerful nonverbal way for human to transmit information and reveal emotion. In this paper, we focus on geometric positions of key parts of the face. Firstly, the face area is detected in a photo or video, then the key parts of the face is extracted and the position correction is performed. A set of key points is located using the relative position of the face. This process can not only effectively avoid the impact of the environment and the light on the sample, but also greatly improve the recognition of facial expressions. With the development of human-computer interaction, facial expression recognition has become a hot topic in the field of pattern recognition. After years of development, facial expression recognition has achieved some success such as HOG. In this paper, HOG feature extraction method of facial expression is used as a contrast. Experimental results show that the proposed algorithm can extract key information and achieves higher recognition accuracy.
Year
Venue
Keywords
2017
2017 10TH INTERNATIONAL CONGRESS ON IMAGE AND SIGNAL PROCESSING, BIOMEDICAL ENGINEERING AND INFORMATICS (CISP-BMEI)
component: expression recognition, key point, SVM, HOG
Field
DocType
Citations 
Muscle activity,Computer vision,Facial recognition system,Pattern recognition,Facial expression recognition,Computer science,Support vector machine,Algorithm,Feature extraction,Facial expression,Artificial intelligence
Conference
1
PageRank 
References 
Authors
0.35
0
6
Name
Order
Citations
PageRank
Caiyou Yuan110.69
QingXiang Wu24412.42
Caiyun Wu3168.46
Peng-Fei Li45620.94
Yanan Zhang596.92
Yao Xiao6137.62