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Multimodal Approach for Face Recognition using 3D-2D Face Feature Fusion
Naveen S, R.S Moni
Pages - 73 - 86     |    Revised - 31-03-2014     |    Published - 30-04-2014
Volume - 8   Issue - 3    |    Publication Date - June 2014  Table of Contents
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KEYWORDS
Point Cloud, Rotation Invariance, Pose Correction, Depth Map, Spectral Transformations, , Texture Map and Principal Component Analysis.
ABSTRACT
3D Face recognition has been an area of interest among researchers for the past few decades especially in pattern recognition. The main advantage of 3D Face recognition is the availability of geometrical information of the face structure which is more or less unique for a subject. This paper focuses on the problems of person identification using 3D Face data. Use of unregistered 3D Face data for feature extraction significantly increases the operational speed of the system with huge database enrollment. In this work, unregistered 3D Face data is fed to a classifier in multiple spectral representations of the same data. Discrete Fourier Transform (DFT) and Discrete Cosine Transform (DCT) are used for the spectral representations. The face recognition accuracy obtained when the feature extractors are used individually is evaluated. The use of depth information alone in different spectral representation was not sufficient to increase the recognition rate. So a fusion of texture and depth information of face is proposed. Fusion of the matching scores proves that the recognition accuracy can be improved significantly by fusion of scores of multiple representations. FRAV3D database is used for testing the algorithm.
CITED BY (2)  
1 Naveen, S., & Moni, R. S. (2016). Contourlet and Fourier Transform Features Based 3D Face Recognition System. In Intelligent Systems Technologies and Applications (pp. 411-425). Springer International Publishing.
2 Naveen, S., Nair, S. S., & Moni, R. S. (2015, August). 3D face recognition using optimised directional faces and fourier transform. In Advances in Computing, Communications and Informatics (ICACCI), 2015 International Conference on (pp. 1856-1861). IEEE.
1 Google Scholar 
2 CiteSeerX 
3 refSeek 
4 TechRepublic 
5 Scribd 
6 SlideShare 
7 PdfSR 
1 http://www.frav.es/databases/FRAV3D
2 Alexander M. Bronstein; Michael M. Bronstein and Ron Kimmel. “Expression-invariant 3D face recognition.” In Proc. International Conference on Audio- and Video-based Biometric Person Authentication, volume 2688 of Lecture Notes in Computer Science Guildford, UK,2003, pp:62-70.
3 C. Beumier, “3D face recognition” In IEEE Int. Conf. on Computational Intelligence for Homeland Security and Personal Safety (CIHSPS2004), Venice, Italy, Jul 2004, pp:21-22.
4 Gang Pan; Shi Han Zhaohui Wu and Yueming Wang. “3D Face Recognition using Mapped Depth Images.” Proceedings of the IEEEComputer Society Conference on CVPR (CVPR’05)Workshops- Volume- 03, 2005 p:175.
5 Xue Yuan;Jianming Lu and Takashi Yahagi. “A Method of 3D Face Recognition Based on Principal Component Analysis Algorithm.” IEEE International Symposium on Circuits and Systems, Vol. 4 May 2005. pp: 3211 - 3214.
6 Trina Russ; Chris Boehnen and Tanya Peters. “3D Face Recognition Using 3D Alignment for PCA”, Proceedings of the 2006 IEEE Computer Society Conference on CVPR (CVPR’06)Volume 2, 2006 pp: 1391 – 1398.
7 Ajmal Mian; Mohammed Bennamoun and Robyn Owens. ”Automatic 3D Face Detection,Normalization and Recognition.” Proceedings of the Third International Symposium on 3DPVT (3DPVT'06) Jun 2006, pp: 735-742.
8 Ondrej Smirg, Jan Mikulka, Marcos Faundez-Zanuy, Marco Grassi and Jiri Mekyska. “Gender Recognition Using PCA and DCT of Face Images.” Advances in Computational Intelligence ,Lecture Notes in Computer Science Volume 6692, 2011, pp: 220-227.
9 Hua Gao, Hazim Kemal Ekenel and Rainer Stiefelhagen. “Pose Normalization for Local Appearance-Based Face Recognition.” Advances in Biometrics, Lecture Notes in Computer Science Volume 5558, 2009, pp:32-41.
10 Mohammad Naser-Moghaddasi Yashar Taghizadegan and Hassan Ghassemian.(2012, Feb),.”3D Face Recognition Method Using 2DPCA-Euclidean Distance Classification”, ACEEE International Journal on Control System and Instrumentation(Vol 3), Available:http://hal.archives-ouvertes.fr/docs/00/74/16/40/PDF/70.pdf.
11 Omid Gervei, Ahmad Ayatollahi and Navid Gervei.”3D Face Recognition Using Modified PCA Methods” World Academy of Science, Engineering & Technology; Mar 2010, Issue 39, p264
12 M. Turk and A. Pentland, “Eigenfaces for recognition”, J. Cognitive Neuroscience , 1991,3(1), pp. 71 – 86.
13 Image Analysis and Recognition, Third International Conference, ICIAR 2006, Póvoa de Varzim, Portugal, September 18-20, 2006, Proceedings, Part II, Lecture Notes in Computer Science , Volume 4142 2006.
14 Y. Wang, C. Chua and Y. Ho, “Facial feature detection and face recognition from 2D and 3D images”, Pattern Recognition Letters, vol. 23, 2002, pp. 1191-1202.
15 K. I. Chang, K. W. Bowyer and P. J. Flynn, “Multiple nose region matching for 3D face recognition under varying facial expression”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 28, no. 10, 2006, pp. 1695-1700.
16 C. McCool, J. Cook, V. Chandran and S. Sridharan, “Combined 2D / 3D Face Recognition using Log-Gabor Templates”, Proceedings of the IEEE International Conference on Video and Signal Based Surveillance (AVSS'06), 2006, pp. 83.
17 C. McCool, J. Cook, V. Chandran and S. Sridharan, “Feature Modelling of PCA Difference Vectors for 2D and 3D Face Recognition”, in Proceedings of IEEE International Conference on Video and Signal Based Surveillance, 2006, pp. 57.
18 Pamplona Segundo, M , Silva, L and Bellon, O.R.P, “ Real-time scale-invariant face detection on range images” , in IEEE International Conference on Systems, Man and Cybernetics (SMC), 2011 , pp:914 - 919.
19 Jahanbin, S, Hyohoon Cho, Bovik, A.C, “Passive Multimodal 2-D+3-D Face Recognition Using Gabor Features and Landmark Distances”, in IEEE Transactions on Information Forensics and Security(Volume:6,Issue: 4 ), 2011, pp: 1287 - 1304.
Mr. Naveen S
LBS Institute of Technology for Women - India
nsnair11176@gmail.com
Dr. R.S Moni
Professor, Dept. of ECE Marian Engineering College, Trivandrum, Kerala, India - India