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International Journal of Image, Graphics and Signal Processing(IJIGSP)

ISSN: 2074-9074 (Print), ISSN: 2074-9082 (Online)

Published By: MECS Press

IJIGSP Vol.6, No.7, Jun. 2014

3D Face Recognition based on Radon Transform, PCA, LDA using KNN and SVM

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Author(s)

P. S. Hiremath, Manjunatha Hiremath

Index Terms

3D face recognition;range images;Radon Transform;Principal Component Analysis; Linear Discriminant Analysis;KNN;SVM

Abstract

Biometrics (or biometric authentication) refers to the identification of humans by their characteristics or traits. Bio-metrics is used in computer science as a form of identification and access control. It is also used to identify individuals in groups that are under surveillance. Biometric identifiers are the distinctive, measurable characteristics used to label and describe individuals. 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. In the previous research works, there are several methods for face recognition using range images that are limited to the data acquisition and pre-processing 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 three publicly available databases, namely, Bhosphorus, Texas and CASIA 3D face databases. The experimental results are shown 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 Eigen faces of PCA and 5 LDA components lead to an average recognition rate of 99.20% using SVM classifier.

Cite This Paper

P. S. Hiremath, Manjunatha Hiremath,"3D Face Recognition based on Radon Transform, PCA, LDA using KNN and SVM", IJIGSP, vol.6, no.7, pp.36-43, 2014.DOI: 10.5815/ijigsp.2014.07.05

Reference

[1]R. Chellappa, C. Wilson, and S. Sirohey, " Human and machine recognition of faces: A survey", Proc. Of the IEEE, vol. 83, no. 5, pp.704-740(May 1995).

[2]W. Zhao, R. Chellappa, P. J. Phillips, A. Rosenfeld, "Face Recognition: A Literature Survey", ACM Computing Surveys, Vol. 35, No. 4, pp. 399–458(December 2003).

[3]Patil A.M., Kolhe S.R. and Patil P.M., "2D Face Recognition Techniques: A Survey", International Journal of Machine Intelligence, ISSN: 0975–2927, Volume 2, Issue 1, pp.74-83(2010).

[4]Andrea F. Abate, Michele Nappi, Daniel Riccio, Gabriele Sabatino, "2D and 3D face recognition: A survey", Pattern Recognition Letters 28, pp.1885–1906, (2007).

[5]Kevin W. Bowyer, Kyong Chang, Patrick Flynn, "A survey of approaches and challenges in 3D and multi-modal 3D + 2D face recognition", Computer Vision and Image Understanding 101, 1–15 (2006).

[6]Shalini Gupta, Mia K. Markey, Alan C. Bovik, "Anthropometric 3D Face Recognition", Int. J. Computer Vis., Springer Science+Business Media, LLC (2010).

[7]Xiaoguang Lu , Dirk Colbry , Anil K. Jain, "Matching 2.5D Scans for Face Recognition", Int. Conf. Pattern Recog. (ICPR 2004), pp.362-366.

[8]Kyong I. Chang , Bowyer, K.W., Flynn P. J., "Adaptive Rigid Multi-region Selection for Handling Expression Variation in 3D Face Recognition", Computer Vision and Pattern Recognition - Workshops, 2005. 

[9]Jahanbim, S., Hyohoon Choi , Jahanbin R., Bovik A. C., "Automated facial feature detection and face recognition using Gabor features on range and portrait images", 15th IEEE International Conference on Image Processing (ICIP 2008).

[10]N. Alyüz, B. Gökberk, H. Dibeklioğlu, A. Savran, A. A. Salah, L. Akarun, B. Sankur, "3D Face Recognition Benchmarks on the Bosphorus Database with Focus on Facial Expressions" , The First COST 2101 Workshop on Biometrics and Identity Management (BIOID 2008), Roskilde University, Denmark, May 2008.

[11]S. Gupta, K. R. Castleman, M. K. Markey, A. C. Bovik, "Texas 3D Face Recognition Database", IEEE Southwest Symposium on Image Analysis and Interpretation, May 2010, p 97-100, Austin, TX. URL: http://live.ece.utexas.edu/research/texas3dfr/index.htm.

[12]ChenghuaXu, Yunhong Wang, Tieniu Tan and Long Quan, Automatic 3D Face Recognition Combining Global Geometric Features with Local Shape Variation Information, Proc. The 6th IEEE International Conference on Automatic Face and Gesture Recognition (FG), pp.308-313, 2004.(CASIA 3D Face Database).

[13]Amir Averbuch and Yoel Shkolnisky, "3D Fourier based discrete Radon transform", Appl. Comput. Harmon. Anal. 15 , Elsevier Inc. (2003), pp. 33–69.

[14]M. Turk, A. Pentland, "Eigenfaces for Recognition", Journal of Cognitive Neurosicence, Vol. 3, No. 1, 1991, pp. 71-86.

[15]H. Moon, P.J. Phillips, "Computational and Performance aspects of PCA-based Face Recognition Algorithms", Perception, Vol. 30, pp. 303-321(2001).

[16]K. Etemad, R. Chellappa, "Discriminant Analysis for Recognition of Human Face Images", Journal of the Optical Society of America A, Vol. 14, No. 8, August 1997, pp. 1724-1733.

[17]W. Zhao, R. Chellappa, A. Krishnaswamy, "Discriminant Analysis of Principal Components for Face Recognition", Proc. of the 3rd IEEE International Conference on Face and Gesture Recognition, FG'98, 14-16 April 1998, Nara, Japan, pp. 336-341.

[18]P. S. Hiremath and Manjunath Hiremath, "3D Face Recognition Using Radon Transform and PCA", International Journal of Graphics & Image Processing,Vol. 2,No. 2,(May 2012), pp. 123-128.

[19]Hengliand Tang, Yanfeng Sun, Baocai Yin and Yun Ge, "3D Face recognition based on Sparse representation", Journal of Supercomputing, Vol. 58, Issue 1, pp.84-95(2011).