Dynamic Texture Recognition Using Local Binary Patterns with an Application to Facial Expressions

Dynamic texture (DT) is an extension of texture to the temporal domain. Description and recognition of DTs have attracted growing attention. In this paper, a novel approach for recognizing DTs is proposed and its simplifications and extensions to facial image analysis are also considered. First, the...

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Veröffentlicht in:	IEEE transactions on pattern analysis and machine intelligence Jg. 29; H. 6; S. 915 - 928
Hauptverfasser:	Guoying Zhao, Pietikainen, M.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Los Alamitos, CA IEEE 01.06.2007 IEEE Computer Society The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithms Applied sciences Artificial Intelligence Biometry - methods Computation Computer science; control theory; systems Computer Security Dynamics Exact sciences and technology Face - anatomy & histology Face - physiology Face recognition Facial Facial Expression facial image analysis Gray-scale Humans Image Enhancement - methods Image Interpretation, Computer-Assisted - methods Image motion analysis Image recognition Image texture analysis local binary pattern Mathematical models motion Motion analysis Pattern analysis Pattern recognition Pattern Recognition, Automated - methods Pattern recognition. Digital image processing. Computational geometry Recognition Reproducibility of Results Robustness Sensitivity and Specificity Signal Processing, Computer-Assisted Spatial databases Surface layer Temporal texture Texture Video Recording - methods Texture analysis Motion estimation Grey level image Image processing motion Local theory Pattern recognition facial image analysis Texture Image analysis Gray scale local binary pattern Facies Database Robustness Localization Cooccurrence analysis Pattern analysis Motion analysis Artificial intelligence Facial expression Temporal texture
ISSN:	0162-8828, 2160-9292, 1939-3539
Online-Zugang:	Volltext
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Abstract	Dynamic texture (DT) is an extension of texture to the temporal domain. Description and recognition of DTs have attracted growing attention. In this paper, a novel approach for recognizing DTs is proposed and its simplifications and extensions to facial image analysis are also considered. First, the textures are modeled with volume local binary patterns (VLBP), which are an extension of the LBP operator widely used in ordinary texture analysis, combining motion and appearance. To make the approach computationally simple and easy to extend, only the co-occurrences of the local binary patterns on three orthogonal planes (LBP-TOP) are then considered. A block-based method is also proposed to deal with specific dynamic events such as facial expressions in which local information and its spatial locations should also be taken into account. In experiments with two DT databases, DynTex and Massachusetts Institute of Technology (MIT), both the VLBP and LBP-TOP clearly outperformed the earlier approaches. The proposed block-based method was evaluated with the Cohn-Kanade facial expression database with excellent results. The advantages of our approach include local processing, robustness to monotonic gray-scale changes, and simple computation
AbstractList	Dynamic texture (DT) is an extension of texture to the temporal domain. Description and recognition of DTs have attracted growing attention. In this paper, a novel approach for recognizing DTs is proposed and its simplifications and extensions to facial image analysis are also considered. First, the textures are modeled with volume local binary patterns (VLBP), which are an extension of the LBP operator widely used in ordinary texture analysis, combining motion and appearance. To make the approach computationally simple and easy to extend, only the co-occurrences of the local binary patterns on three orthogonal planes (LBP-TOP) are then considered. A block-based method is also proposed to deal with specific dynamic events such as facial expressions in which local information and its spatial locations should also be taken into account. In experiments with two DT databases, DynTex and Massachusetts Institute of Technology (MIT), both the VLBP and LBP-TOP clearly outperformed the earlier approaches. The proposed block-based method was evaluated with the Cohn-Kanade facial expression database with excellent results. The advantages of our approach include local processing, robustness to monotonic gray-scale changes, and simple computation Dynamic texture (DT) is an extension of texture to the temporal domain. Description and recognition of DTs have attracted growing attention. In this paper, a novel approach for recognizing DTs is proposed and its simplifications and extensions to facial image analysis are also considered. First, the textures are modeled with volume local binary patterns (VLBP), which are an extension of the LBP operator widely used in ordinary texture analysis, combining motion and appearance. To make the approach computationally simple and easy to extend, only the co-occurrences of the local binary patterns on three orthogonal planes (LBP-TOP) are then considered. A block-based method is also proposed to deal with specific dynamic events such as facial expressions in which local information and its spatial locations should also be taken into account. In experiments with two DT databases, DynTex and Massachusetts Institute of Technology (MIT), both the VLBP and LBP-TOP clearly outperformed the earlier approaches. The proposed block-based method was evaluated with the Cohn-Kanade facial expression database with excellent results. The advantages of our approach include local processing, robustness to monotonic gray-scale changes, and simple computation.Dynamic texture (DT) is an extension of texture to the temporal domain. Description and recognition of DTs have attracted growing attention. In this paper, a novel approach for recognizing DTs is proposed and its simplifications and extensions to facial image analysis are also considered. First, the textures are modeled with volume local binary patterns (VLBP), which are an extension of the LBP operator widely used in ordinary texture analysis, combining motion and appearance. To make the approach computationally simple and easy to extend, only the co-occurrences of the local binary patterns on three orthogonal planes (LBP-TOP) are then considered. A block-based method is also proposed to deal with specific dynamic events such as facial expressions in which local information and its spatial locations should also be taken into account. In experiments with two DT databases, DynTex and Massachusetts Institute of Technology (MIT), both the VLBP and LBP-TOP clearly outperformed the earlier approaches. The proposed block-based method was evaluated with the Cohn-Kanade facial expression database with excellent results. The advantages of our approach include local processing, robustness to monotonic gray-scale changes, and simple computation. Dynamic texture (DT) is an extension of texture to the temporal domain. Description and recognition of DTs have attracted growing attention. In this paper, a novel approach for recognizing DTs is proposed and its simplifications and extensions to facial image analysis are also considered. First, the textures are modeled with volume local binary patterns (VLBP), which are an extension of the LBP operator widely used in ordinary texture analysis, combining motion and appearance. To make the approach computationally simple and easy to extend, only the co-occurrences of the local binary patterns on three orthogonal planes (LBP-TOP) are then considered. A block-based method is also proposed to deal with specific dynamic events such as facial expressions in which local information and its spatial locations should also be taken into account. In experiments with two DT databases, DynTex and Massachusetts Institute of Technology (MIT), both the VLBP and LBP-TOP clearly outperformed the earlier approaches. The proposed block-based method was evaluated with the Cohn-Kanade facial expression database with excellent results. The advantages of our approach include local processing, robustness to monotonic gray-scale changes, and simple computation. [...] the textures are modeled with volume local binary patterns (VLBP), which are an extension of the LBP operator widely used in ordinary texture analysis, combining motion and appearance. Dynamic texture (DT) is an extension of texture to the temporal domain. Description and recognition of DTs have attracted growing attention. In this paper, a novel approach for recognizing DTs is proposed and its simplifications and extensions [abstract truncated by publisher].
Author	Pietikainen, M. Guoying Zhao
Author_xml	– sequence: 1 surname: Guoying Zhao fullname: Guoying Zhao organization: Dept. of Electr. & Inf. Eng., Oulu Univ – sequence: 2 givenname: M. surname: Pietikainen fullname: Pietikainen, M. organization: Dept. of Electr. & Inf. Eng., Oulu Univ
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Keywords	Texture analysis Motion estimation Grey level image Image processing motion Local theory Pattern recognition facial image analysis Texture Image analysis Gray scale local binary pattern Facies Database Robustness Localization Cooccurrence analysis Pattern analysis Motion analysis Artificial intelligence Facial expression Temporal texture
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References	ref13 ref35 ref12 ref34 ref15 ref37 ref14 ref36 ref31 ref11 ref33 ref32 Fujita (ref27) ref2 ref1 ref17 ref16 ref38 ref19 ref18 Lu (ref21); 2 Peh (ref22) Feng (ref10) 2005; 15 Smith (ref28); 2 ref24 ref23 ref26 ref20 Fazekas (ref25) ref29 ref8 ref7 ref9 ref4 ref3 ref6 ref5 Zhong (ref30) 2002
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Snippet	Dynamic texture (DT) is an extension of texture to the temporal domain. Description and recognition of DTs have attracted growing attention. In this paper, a... [...] the textures are modeled with volume local binary patterns (VLBP), which are an extension of the LBP operator widely used in ordinary texture analysis,...
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SubjectTerms	Algorithms Applied sciences Artificial Intelligence Biometry - methods Computation Computer science; control theory; systems Computer Security Dynamics Exact sciences and technology Face - anatomy & histology Face - physiology Face recognition Facial Facial Expression facial image analysis Gray-scale Humans Image Enhancement - methods Image Interpretation, Computer-Assisted - methods Image motion analysis Image recognition Image texture analysis local binary pattern Mathematical models motion Motion analysis Pattern analysis Pattern recognition Pattern Recognition, Automated - methods Pattern recognition. Digital image processing. Computational geometry Recognition Reproducibility of Results Robustness Sensitivity and Specificity Signal Processing, Computer-Assisted Spatial databases Surface layer Temporal texture Texture Video Recording - methods
Title	Dynamic Texture Recognition Using Local Binary Patterns with an Application to Facial Expressions
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