Novel low level local features for 3D expression invariant face recognition

Munawar Hayat, Mohammed Bennamoun, Yinjie Lei, Amar El-Sallam

Research output: A Conference proceeding or a Chapter in BookConference contribution

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Abstract

In this paper, we present a system based on novel low level local features to recognize 3D faces under varying facial expressions. Our local features are obtained by combinatorially selecting two points from expression insensitive semi-rigid portions of the face. The curve length between the two points is computed and the distribution of such curve lengths is used as a feature vector to model the geometric shape distribution of the face. Our proposed features are very simple to compute yet highly distinctive and discriminating. Kernel Fisher discriminant analysis is used for feature optimization, followed by a linear support vector machine classifier for recognition. The system is extensively tested on 2500 facial scans of BU 3DFE dataset. Our experimental results show that the proposed system achieves a very high average classification rate of 99.17% and verification rates of 99.0% and above for a false acceptance rate of 0.001.
Original languageEnglish
Title of host publication2012 12th international conference on control automation robotics & vision (ICARCV)
EditorsDanwei Wang, Chien Chern Cheah
Place of PublicationChina
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages194-198
Number of pages5
ISBN (Electronic)9781467318723
ISBN (Print)9781467318716
DOIs
Publication statusPublished - 5 Dec 2012
Externally publishedYes
Event12th international conference on control automation robotics and vision ICARCV 2012 - Guangzhou, Guangzhou, China
Duration: 5 Dec 20127 Dec 2012

Conference

Conference12th international conference on control automation robotics and vision ICARCV 2012
CountryChina
CityGuangzhou
Period5/12/127/12/12

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Discriminant analysis
Face recognition
Support vector machines
Classifiers

Cite this

Hayat, M., Bennamoun, M., Lei, Y., & El-Sallam, A. (2012). Novel low level local features for 3D expression invariant face recognition. In D. Wang, & C. C. Cheah (Eds.), 2012 12th international conference on control automation robotics & vision (ICARCV) (pp. 194-198). China: IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/ICARCV.2012.6485157
Hayat, Munawar ; Bennamoun, Mohammed ; Lei, Yinjie ; El-Sallam, Amar. / Novel low level local features for 3D expression invariant face recognition. 2012 12th international conference on control automation robotics & vision (ICARCV). editor / Danwei Wang ; Chien Chern Cheah. China : IEEE, Institute of Electrical and Electronics Engineers, 2012. pp. 194-198
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title = "Novel low level local features for 3D expression invariant face recognition",
abstract = "In this paper, we present a system based on novel low level local features to recognize 3D faces under varying facial expressions. Our local features are obtained by combinatorially selecting two points from expression insensitive semi-rigid portions of the face. The curve length between the two points is computed and the distribution of such curve lengths is used as a feature vector to model the geometric shape distribution of the face. Our proposed features are very simple to compute yet highly distinctive and discriminating. Kernel Fisher discriminant analysis is used for feature optimization, followed by a linear support vector machine classifier for recognition. The system is extensively tested on 2500 facial scans of BU 3DFE dataset. Our experimental results show that the proposed system achieves a very high average classification rate of 99.17{\%} and verification rates of 99.0{\%} and above for a false acceptance rate of 0.001.",
keywords = "Face-recognition, Pattern-recognition, Computer-vision",
author = "Munawar Hayat and Mohammed Bennamoun and Yinjie Lei and Amar El-Sallam",
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language = "English",
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booktitle = "2012 12th international conference on control automation robotics & vision (ICARCV)",
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Hayat, M, Bennamoun, M, Lei, Y & El-Sallam, A 2012, Novel low level local features for 3D expression invariant face recognition. in D Wang & CC Cheah (eds), 2012 12th international conference on control automation robotics & vision (ICARCV). IEEE, Institute of Electrical and Electronics Engineers, China, pp. 194-198, 12th international conference on control automation robotics and vision ICARCV 2012, Guangzhou, China, 5/12/12. https://doi.org/10.1109/ICARCV.2012.6485157

Novel low level local features for 3D expression invariant face recognition. / Hayat, Munawar; Bennamoun, Mohammed; Lei, Yinjie; El-Sallam, Amar.

2012 12th international conference on control automation robotics & vision (ICARCV). ed. / Danwei Wang; Chien Chern Cheah. China : IEEE, Institute of Electrical and Electronics Engineers, 2012. p. 194-198.

Research output: A Conference proceeding or a Chapter in BookConference contribution

TY - GEN

T1 - Novel low level local features for 3D expression invariant face recognition

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AU - Bennamoun, Mohammed

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AU - El-Sallam, Amar

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N2 - In this paper, we present a system based on novel low level local features to recognize 3D faces under varying facial expressions. Our local features are obtained by combinatorially selecting two points from expression insensitive semi-rigid portions of the face. The curve length between the two points is computed and the distribution of such curve lengths is used as a feature vector to model the geometric shape distribution of the face. Our proposed features are very simple to compute yet highly distinctive and discriminating. Kernel Fisher discriminant analysis is used for feature optimization, followed by a linear support vector machine classifier for recognition. The system is extensively tested on 2500 facial scans of BU 3DFE dataset. Our experimental results show that the proposed system achieves a very high average classification rate of 99.17% and verification rates of 99.0% and above for a false acceptance rate of 0.001.

AB - In this paper, we present a system based on novel low level local features to recognize 3D faces under varying facial expressions. Our local features are obtained by combinatorially selecting two points from expression insensitive semi-rigid portions of the face. The curve length between the two points is computed and the distribution of such curve lengths is used as a feature vector to model the geometric shape distribution of the face. Our proposed features are very simple to compute yet highly distinctive and discriminating. Kernel Fisher discriminant analysis is used for feature optimization, followed by a linear support vector machine classifier for recognition. The system is extensively tested on 2500 facial scans of BU 3DFE dataset. Our experimental results show that the proposed system achieves a very high average classification rate of 99.17% and verification rates of 99.0% and above for a false acceptance rate of 0.001.

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Hayat M, Bennamoun M, Lei Y, El-Sallam A. Novel low level local features for 3D expression invariant face recognition. In Wang D, Cheah CC, editors, 2012 12th international conference on control automation robotics & vision (ICARCV). China: IEEE, Institute of Electrical and Electronics Engineers. 2012. p. 194-198 https://doi.org/10.1109/ICARCV.2012.6485157