Paper Info
Title
Facial expression recognition using histogram variances faces
Abstract
In human's expression recognition, the representation of expression features is essential for the recognition accuracy. In this work we propose a novel approach for extracting expression dynamic features from facial expression videos. Rather than utilising statistical models e.g. Hidden Markov Model (HMM), our approach integrates expression dynamic features into a static image, the Histogram Variances Face (HVF), by fusing histogram variances among the frames in a video. The HVFs can be automatically obtained from videos with different frame rates and immune to illumination interference. In our experiments, for the videos picturing the same facial expression, e.g., surprise, happy and sadness etc., their corresponding HVFs are similar, even though the pupperformers and frame rates are different. Therefore the static facial recognition approaches can be utilised for the dynamic expression recognition. We have applied this approach on the well-known Cohn-Kanade AU-Coded Facial Expression database then classified HVFs using PCA and Support Vector Machine (SVMs), and found the accuracy of HVFs classification is very encouraging.
Year
DOI
Venue
2009
10.1109/WACV.2009.5403081
WACV
Keywords
Field
DocType
histogram variances face,facial expression recognition,face recognition,emotion recognition,support vector machine,principal component analysis,support vector machines,hidden markov model,hidden markov models,histograms,statistical model,databases,pixel,facial expression
Facial recognition system,Computer vision,Histogram,Pattern recognition,Computer science,Support vector machine,Facial expression,Artificial intelligence,Pixel,Frame rate,Statistical model,Hidden Markov model
Conference
ISSN
ISBN
Citations 
1550-5790
978-1-4244-5497-6
2
PageRank 
References 
Authors
0.40
10
5
Name
Order
Citations
PageRank
Ruo Du172.52
Qiang Wu22014.06
Xiangjian He3932132.03
Wenjing Jia432545.08
Daming Wei521544.97