A Comparative Study: Face Recognition from Video Sequences using HMM

Takalkar, M; Nagori, M; Ashok, V

A Comparative Study: Face Recognition from Video Sequences using HMM

Takalkar, M

Nagori, M Ashok, V

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Publisher:: IFRSA’s International Journal of Computing (IIJC)
Publication Type:: Journal Article
Citation:: IFRSA’s International Journal of Computing (IIJC), 2013, 2 (4), pp. 25 - 30 (6)
Issue Date:: 2013-01-03

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Field	Value	Language
dc.contributor.author	Takalkar, M https://orcid.org/0000-0002-3413-0730	en_US
dc.contributor.author	Nagori, M	en_US
dc.contributor.author	Ashok, V	en_US
dc.date.available	2012-10-08	en_US
dc.date.issued	2013-01-03	en_US
dc.identifier.citation	IFRSA’s International Journal of Computing (IIJC), 2013, 2 (4), pp. 25 - 30 (6)	en_US
dc.identifier.issn	2231-2153	en_US
dc.identifier.uri	http://hdl.handle.net/10453/121853
dc.description.abstract	While traditional face recognition is typically based on still images, face recognition from video sequences has become popular recently. This work describes a new method to perform face recognition from video sequences. Faces are detected and tracked in a video sequence using Hidden Markov Model. This process also allows locating and extracting facial feature regions around the eyes, nose and mouth. During the training process, the statistics of training video sequences of each subject, and the temporal dynamics, are learned by an HMM. During the recognition process, the temporal characteristics of the test video sequence are analyzed over time by the HMM corresponding to each subject. Conventional techniques such as Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), Independent Component Analysis (ICA), and feature based Elastic Bunch Graph Matching (EBGM) and 2D and 3D face models are well known for face detection and recognition.	en_US
dc.publisher	IFRSA’s International Journal of Computing (IIJC)	en_US
dc.relation.ispartof	IFRSA’s International Journal of Computing (IIJC)	en_US
dc.title	A Comparative Study: Face Recognition from Video Sequences using HMM	en_US
dc.type	Journal Article
utslib.citation.volume	4	en_US
utslib.citation.volume	2	en_US
utslib.location.activity	India	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	closed_access
pubs.consider-herdc	true	en_US
pubs.issue	4	en_US
pubs.publication-status	Published online	en_US
pubs.volume	2	en_US

Abstract:

While traditional face recognition is typically based on still images, face recognition from video sequences has become popular recently. This work describes a new method to perform face recognition from video sequences. Faces are detected and tracked in a video sequence using Hidden Markov Model. This process also allows locating and extracting facial feature regions around the eyes, nose and mouth. During the training process, the statistics of training video sequences of each subject, and the temporal dynamics, are learned by an HMM. During the recognition process, the temporal characteristics of the test video sequence are analyzed over time by the HMM corresponding to each subject. Conventional techniques such as Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), Independent Component Analysis (ICA), and feature based Elastic Bunch Graph Matching (EBGM) and 2D and 3D face models are well known for face detection and recognition.

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http://hdl.handle.net/10453/121853