Paper Info
Title
Nonparametric discriminant HMM and application to facial expression recognition
Abstract
This paper presents a nonparametric discriminant HMM and applies it to facial expression recognition. In the proposed HMM, we introduce an effective nonparametric output probability estimation method to increase the discrimination ability at both hidden state level and class level. The proposed method uses a nonparametric adaptive kernel to utilize information from all classes and improve the discrimination at class level. The discrimination between hidden states is increased by defining membership coefficients which associate each reference vector with hidden states. The adaption of such coefficients is obtained by the expectation maximization (EM) method. Furthermore, we present a general formula for the estimation of output probability, which provides a way to develop new HMMs. Finally, we evaluate the performance of the proposed method on the CMU expression database and compare it with other nonparametric HMMs.
Year
DOI
Venue
2009
10.1109/CVPR.2009.5206509
CVPR
Keywords
Field
DocType
expectation-maximisation algorithm,nonparametric discriminant hmm,facial expression recognition,face recognition,expectation maximization method,nonparametric adaptive kernel,nonparametric output probability estimation method,emotion recognition,hidden markov models,hidden markov model,probability,databases,parameter estimation,estimation,entropy,application software,mathematical model,expectation maximization,kernel,image recognition,computer science,quadratic programming
Kernel (linear algebra),Facial recognition system,Pattern recognition,Discriminant,Expectation–maximization algorithm,Computer science,Nonparametric statistics,Artificial intelligence,Estimation theory,Quadratic programming,Hidden Markov model
Conference
Volume
Issue
ISSN
2009
1
1063-6919
ISBN
Citations 
PageRank 
978-1-4244-3992-8
23
0.98
References 
Authors
19
2
Name
Order
Citations
PageRank
Lifeng Shang148530.96
Kwok Ping Chan231323.52