Fully automatic face recognition from 3D videos

Munawar Hayat, Mohammed Bennamoun, Amar El-Sallam

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

5 Citations (Scopus)
2 Downloads (Pure)

Abstract

Almost all of the existing research on 3D face recognition is based on static 3D images. 3D videos are believed to provide more information in terms of both the shape as well as the dynamics of an individual's face. This paper presents a system which exploits the spatiotemporal information in 3D videos for the task of face recognition. An algorithm for automatic normalization of raw 3D videos is also given. After the detection of the nose tip, all meshes of the 3D video are cropped and uniformly sampled to form range videos. Spatiotemporal Local Binary Pattern (LBP) descriptors are used for feature extraction from the range videos. For classification, a linear multiclass Support Vector Machine (SVM) is used. The system is trained on videos of a person with different facial expressions and tested on a video with new facial expression. Experimental results on the largest currently available 3D video database, BU 4DFE, show a high recognition rate of 92.68%.
Original languageEnglish
Title of host publication2012 21st International conference on Pattern recognition (ICPR 2012)
EditorsJan-Olof Eklundh, Yuichi Ohta, Steven Tanimoto
Place of PublicationTskuba Japan
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages1415-1418
Number of pages4
ISBN (Electronic)9784990644109
ISBN (Print)9781467322164
Publication statusPublished - 2012
Externally publishedYes
Event21st International Conference on Pattern Recognition (ICPR 2012) - Tsukuba, Tsukuba, Japan
Duration: 11 Nov 201215 Nov 2012

Conference

Conference21st International Conference on Pattern Recognition (ICPR 2012)
Abbreviated titleICPR 2012
CountryJapan
CityTsukuba
Period11/11/1215/11/12

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  • Cite this

    Hayat, M., Bennamoun, M., & El-Sallam, A. (2012). Fully automatic face recognition from 3D videos. In J-O. Eklundh, Y. Ohta, & S. Tanimoto (Eds.), 2012 21st International conference on Pattern recognition (ICPR 2012) (pp. 1415-1418). IEEE, Institute of Electrical and Electronics Engineers.