NDLI: Non-frontal view facial expression recognition based on ergodic hidden Markov model supervectors

Content Provider	IEEE Xplore Digital Library
Author	Hao Tang Hasegawa-Johnson, M. Huang, T.
Copyright Year	2010
Description	Author affiliation: Department of Electrical and Computer Engineering University of Illinois at Urbana-Champaign (Hao Tang; Hasegawa-Johnson, M.; Huang, T.)
Abstract	Automatic facial expression recognition from non-frontal views is a challenging research topic which has recently started to attract the attention of the research community. In this paper, we propose a novel approach to tackling this problem based on the ergodic hidden Markov model (EHMM) supervector representation of facial images. First, the scale-invariant feature transform (SIFT) feature vectors are extracted from a dense grid of every facial images. Next, an EHMM is trained over all facial images in the training set and is referred to as the universal background model (UBM). The UBM is then maximum a posteriori adapted to each facial image in the training and test sets to produce the image-specific EHMMs. Based on these EHMMs, we derive a supervector representation of the facial images by means of an upper bound approximation of the Kullback-Leibler divergence rate between two EHMMs. Finally, facial expression recognition is performed in the linear discriminant subspace of the EHMM supervectors using the k-nearest-neighbor classification algorithm. Our experiments of recognizing six universal facial expressions over extensive multiview facial images with seven pan angles (−45° ∼ +45°) and five tilt angles (−30° ∼ +30°), which are synthesized from the BU-3DFE facial expression database, show promising results compared to the state of the arts recently reported.
Starting Page	1202
Ending Page	1207
File Size	2087255
Page Count	6
File Format	PDF
ISBN	9781424474912
ISSN	1945788X
e-ISBN	9781424474936
DOI	10.1109/ICME.2010.5582576
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-07-19
Publisher Place	Singapore
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Face recognition Feature extraction Hidden Markov models Databases Training Adaptation model Image recognition supervector representation Facial expression recognition hidden Markov model
Content Type	Text
Resource Type	Article

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