Abstract
Face recognition research started in the 70s and a number of algorithms/systems have been developed in the last decade. Three Dimensional (3D) human face recognition is emerging as a significant biometric technology. Research interest into 3D face recognition has increased during recent years due to the availability of improved 3D acquisition devices and processing algorithms. Three Dimensional face recognition also helps to resolve some of the issues associated with two dimensional (2D) face recognition. Since 2D systems employ intensity images, their performance is reported to degrade significantly with variations in facial pose and ambient illumination. The 3D face recognition systems, on the other hand, have been reported to be less sensitive to the changes in the ambient illumination during image capture that the 2D systems. In the previous works, there are several methods for face recognition using range images that are limited to the data acquisition and preprocessing stage only. In the present paper, we have proposed a 3D face recognition algorithm which is based on Radon transform, principal component analysis (PCA) and linear discriminant analysis (LDA). The radon transform (RT) is a fundamental tool to normalize 3D range data. The PCA is used to reduce the dimensionality of feature space, and the LDA is used to optimize the features, which are finally used to recognize the faces. The experimentation has been done using Texas 3D face database. The experimental results show that the proposed algorithm is efficient in terms of accuracy and detection time, in comparison with other methods based on PCA only and RT + PCA. It is observed that 40 eigenfaces of PCA and 5 LDA components lead to an average recognition rate of 99.16 %.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Chellappa R, Wilson C, Sirohey S (1995) Human and machine recognition of faces: a survey. Proc IEEE 83(5):704–740
Zhao W, Chellappa R, Phillip PJ, Rosenfeld A (2003) Face recognition: a literature survey. ACM Comput Surv 35(4):399–458
Patil AM, Kolhe SR, Patil PM (2010) 2D face recognition techniques: a survey, Int J Mach Intell, ISSN: 0975–2927, 2(1):74–83
Abate AF, Nappi M, Riccio D, Sabatino G (2007) 2D and 3D face recognition: a survey. Patt Recog Lett 28:885–1906
Bowyer KW, Chang K, Flynn P (2006) A survey of approaches and challenges in 3D and multi-modal 3D + 2D face recognition. Comput Vis Image Underst 101:1–15
Gupta S, Markey MK, Bovik AC (2010) Anthropometric 3D face recognition. Int J Comput Vis. Springer Science + Business Media, LLC
Lu X, Colbry D, Jain AK (2004) Matching 2.5D scans for face recognition, Int Conf Pattern Recog, 362–366
Chang KI, Bowyer KW, Flynn PJ (2005) Adaptive rigid multi-region selection for handling expression variation in 3D face recognition. Comput Vision Pattern Recogn—Workshops
Jahanbim S, Choi H, Jahanbin R, Bovik AC (2008) Automated facial feature detection and face recognition using Gabor features on range and portrait images, 15th IEEE international conference on image processing
Hiremath PS, Hiremath M (2012) 3D face recognition using radon transform and PCA. Int J Graph Image Process 2(2):123–128
Tang H, Sun Y, Yin B, Ge Y (2011) 3D face recognition based on sparse representation, J Supercomputing, 58(1):84–95
Gupta S, Castleman KR, Markey MK, Bovik AC (2010) Texas 3D face recognition database. IEEE southwest symposium on image analysis and interpretation, 97–100, Austin. URL:http://live.ece.utexas.edu/research/texas3dfr/index.htm
Averbuch A, Shkolnisky Y (2003) 3D fourier based discrete radon transform, Applied and Computational Harmonic Analysis 15, Elsevier, Amsterdam, 33–69
Turk M, Pentland A (1991) Eigenfaces for recognition. J Cognitive Neurosci 3(1):71–86
Moon H, Phillips PJ (2001) Computational and performance aspects of PCA-based face recognition algorithms. Perception 30:303–321
Etemad K, Chellappa R (1997) Discriminant analysis for recognition of human face images. J Opt Soc Am A 14(8):1724–1733
Zhao W, Chellappa R, Krishnaswamy A (1998) Discriminant analysis of principal components for face recognition. Proceedings of the 3rd IEEE international conference on face and gesture recognition, FG’98, 14–16 April 1998, Nara, Japan, pp 336–341
Acknowledgments
The authors are grateful to the referees for their helpful comments and suggestions. Also, the authors are indebted to the University Grants Commission, New Delhi, for the financial support for this research work under UGC-MRP F.No.39-124/2010 (SR).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer India
About this paper
Cite this paper
Hiremath, P.S., Hiremath, M. (2013). Linear Discriminant Analysis for 3D Face Recognition Using Radon Transform. In: Swamy, P., Guru, D. (eds) Multimedia Processing, Communication and Computing Applications. Lecture Notes in Electrical Engineering, vol 213. Springer, New Delhi. https://doi.org/10.1007/978-81-322-1143-3_9
Download citation
DOI: https://doi.org/10.1007/978-81-322-1143-3_9
Published:
Publisher Name: Springer, New Delhi
Print ISBN: 978-81-322-1142-6
Online ISBN: 978-81-322-1143-3
eBook Packages: EngineeringEngineering (R0)