A Novel Maximum Entropy Markov Model for Human Facial Expression Recognition

Research in video based FER systems has exploded in the past decade. However, most of the previous methods work well when they are trained and tested on the same dataset. Illumination settings, image resolution, camera angle, and physical characteristics of the people differ from one dataset to anot...

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Main Authors:	Siddiqi, Muhammad Hameed, Alam, Md. Golam Rabiul, Hong, Choong Seon, Khan, Adil Mehmood, Choo, Hyunseung
Format:	Online
Language:	English
Published:	Public Library of Science 2016
Online Access:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5026367/

id	pubmed-5026367
recordtype	oai_dc
spelling	pubmed-50263672016-09-27 A Novel Maximum Entropy Markov Model for Human Facial Expression Recognition Siddiqi, Muhammad Hameed Alam, Md. Golam Rabiul Hong, Choong Seon Khan, Adil Mehmood Choo, Hyunseung Research Article Research in video based FER systems has exploded in the past decade. However, most of the previous methods work well when they are trained and tested on the same dataset. Illumination settings, image resolution, camera angle, and physical characteristics of the people differ from one dataset to another. Considering a single dataset keeps the variance, which results from differences, to a minimum. Having a robust FER system, which can work across several datasets, is thus highly desirable. The aim of this work is to design, implement, and validate such a system using different datasets. In this regard, the major contribution is made at the recognition module which uses the maximum entropy Markov model (MEMM) for expression recognition. In this model, the states of the human expressions are modeled as the states of an MEMM, by considering the video-sensor observations as the observations of MEMM. A modified Viterbi is utilized to generate the most probable expression state sequence based on such observations. Lastly, an algorithm is designed which predicts the expression state from the generated state sequence. Performance is compared against several existing state-of-the-art FER systems on six publicly available datasets. A weighted average accuracy of 97% is achieved across all datasets. Public Library of Science 2016-09-16 /pmc/articles/PMC5026367/ /pubmed/27635654 http://dx.doi.org/10.1371/journal.pone.0162702 Text en © 2016 Siddiqi et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
repository_type	Open Access Journal
institution_category	Foreign Institution
institution	US National Center for Biotechnology Information
building	NCBI PubMed
collection	Online Access
language	English
format	Online
author	Siddiqi, Muhammad Hameed Alam, Md. Golam Rabiul Hong, Choong Seon Khan, Adil Mehmood Choo, Hyunseung
spellingShingle	Siddiqi, Muhammad Hameed Alam, Md. Golam Rabiul Hong, Choong Seon Khan, Adil Mehmood Choo, Hyunseung A Novel Maximum Entropy Markov Model for Human Facial Expression Recognition
author_facet	Siddiqi, Muhammad Hameed Alam, Md. Golam Rabiul Hong, Choong Seon Khan, Adil Mehmood Choo, Hyunseung
author_sort	Siddiqi, Muhammad Hameed
title	A Novel Maximum Entropy Markov Model for Human Facial Expression Recognition
title_short	A Novel Maximum Entropy Markov Model for Human Facial Expression Recognition
title_full	A Novel Maximum Entropy Markov Model for Human Facial Expression Recognition
title_fullStr	A Novel Maximum Entropy Markov Model for Human Facial Expression Recognition
title_full_unstemmed	A Novel Maximum Entropy Markov Model for Human Facial Expression Recognition
title_sort	novel maximum entropy markov model for human facial expression recognition
description	Research in video based FER systems has exploded in the past decade. However, most of the previous methods work well when they are trained and tested on the same dataset. Illumination settings, image resolution, camera angle, and physical characteristics of the people differ from one dataset to another. Considering a single dataset keeps the variance, which results from differences, to a minimum. Having a robust FER system, which can work across several datasets, is thus highly desirable. The aim of this work is to design, implement, and validate such a system using different datasets. In this regard, the major contribution is made at the recognition module which uses the maximum entropy Markov model (MEMM) for expression recognition. In this model, the states of the human expressions are modeled as the states of an MEMM, by considering the video-sensor observations as the observations of MEMM. A modified Viterbi is utilized to generate the most probable expression state sequence based on such observations. Lastly, an algorithm is designed which predicts the expression state from the generated state sequence. Performance is compared against several existing state-of-the-art FER systems on six publicly available datasets. A weighted average accuracy of 97% is achieved across all datasets.
publisher	Public Library of Science
publishDate	2016
url	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5026367/
_version_	1613653965236338688

A Novel Maximum Entropy Markov Model for Human Facial Expression Recognition

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