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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.1, Nov. 2013

Depth and Intensity Gabor Features Based 3D Face Recognition Using Symbolic LDA and AdaBoost

Full Text (PDF, 301KB), PP.32-39


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Author(s)

P. S. Hiremath,Manjunatha Hiremath

Index Terms

3D face recognition;Radon transform;Symbolic LDA;Gabor Filter;AdaBoost

Abstract

In this paper, the objective is to investigate what contributions depth and intensity information make to the solution of face recognition problem when expression and pose variations are taken into account, and a novel system is proposed for combining depth and intensity information in order to improve face recognition performance. In the proposed approach, local features based on Gabor wavelets are extracted from depth and intensity images, which are obtained from 3D data after fine alignment. Then a novel hierarchical selecting scheme embedded in symbolic linear discriminant analysis (Symbolic LDA) with AdaBoost learning is proposed to select the most effective and robust features and to construct a strong classifier. Experiments are performed on the three datasets, namely, Texas 3D face database, Bhosphorus 3D face database and CASIA 3D face database, which contain face images with complex variations, including expressions, poses and longtime lapses between two scans. The experimental results demonstrate the enhanced effectiveness in the performance of the proposed method. Since most of the design processes are performed automatically, the proposed approach leads to a potential prototype design of an automatic face recognition system based on the combination of the depth and intensity information in face images.

Cite This Paper

P. S. Hiremath, Manjunatha Hiremath,"Depth and Intensity Gabor Features Based 3D Face Recognition Using Symbolic LDA and AdaBoost", IJIGSP, vol.6, no.1, pp.32-39, 2014.DOI: 10.5815/ijigsp.2014.01.05

Reference

[1]W. Zhao, R. Chellappa, P.J. Phillips, “A. Rosenfeld, “Face recognition: a literature survey” , ACM Computing Surveys (CSUR), Archive 35 (4) (2003) 399–458.

[2]K. Bowyer, K. Chang, P. Flynn, “A survey of approaches and challenges in 3D and multi-modal 3D+2D face recognition”, Computer Vision and Image Understanding 101 (1) (2006) 1–15.

[3]Y. Wang, C. Chua, Y. Ho, “Facial feature detection and face recognition from 2D and 3D images ”, Pattern Recognition Letters 23 (2002) 1191–1202.

[4]K.I. Chang, K.W. Bowyer, P.J. Flynn, “An evaluation of multi-model 2D+3D biometrics”, IEEE Transactions on Pattern Analysis and Machine Intelligence 27 (4) (2005) 619–624.

[5]K.I. Chang, K.W. Bowyer, P.J. Flynn, “Multiple nose region matching for 3D face recognition under varying facial expression”, IEEE Transactions on Pattern Analysis and Machine Intelligence 28 (10) (2006) 1695–1700.

[6]T.C. Faltemier, K.W. Bowyer, P.J. Flynn, “A region ensemble for 3-D face recognition”, IEEE Transactions on Information Forensics and Security 3 (1) (2008) 62–73.

[7]T. Russ, C. Boehnen, T. Peters, “3D face recognition using 3D alignment for LDA”, Proceedings of the CVPR'06, 2006, pp. 1391–1398.

[8]W. Lin, K. Wong, N. Boston, Y. Hu, “Fusion of summation invariants in 3D human face recognition”, in: Proceedings of the CVPR'06, 2006, pp. 1369–1376.

[9]F.R. Al-Osaimi, M. Bennamouna, A. Miana, “Integration of local and global geometrical cues for 3D face recognition”, Pattern Recognition 41 (3) (2008)1030–1040.

[10]C. Beumier, M. Acheroy, “Automatic 3D face authentication”, Image and Vision Computing 18 (4) (2000) 315–321.

[11]G. Medioni, R. Waupotitsch, “Face modeling and recognition in 3-D”, Proceedings of the AMFG'03, 2003, pp. 232–233.

[12]X. Lu, A.K. Jain, D. Colbry, “Matching 2.5D face scans to 3D models”, IEEE Transactions on Pattern Analysis and Machine Intelligence 28 (1) (2006) 31–43.

[13]Baback Moghaddam, Alex Pentland, “Beyond Euclidean Eigenspaces: Bayesian Matching for Visual Recognition”, Face Recognition, NATO ASI Series Volume 163, 1998, pp 230-243.

[14]T. Maurer, D. Guigonis, I. Maslov, B. Pesenti, A. Tsaregorodtsev, D. West, G. Medioni, “Performance of geometrixActiveID TM 3D face recognition engine on the FRGC data”, in: IEEEWorkshop on Face Recognition Grand Challenge Experiments, 2005, pp. 154–160.

[15]S. Gupta, M. K. Markey, A. C. Bovik, "Anthropometric 3D Face Recognition", International Journal of Computer Vision, 2010, Volume 90, 3:331-349.

[16]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.

[17]S. Gupta, K. R. Castleman, M. K. Markey, A. C. Bovik, "Texas 3D Face RecognitionDatabase", URL:http://live.ece.utexas.Edu/research/texas3dfr/ index.htm.

[18]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.

[19]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).

[20]T.S. Lee, “Image representation using 2D Gabor wavelets”, IEEE Transactions on Pattern Analysis and Machine Intelligence 18 (10) (1996) 959–971.

[21]D.H. Liu, K.M. Lam, L.S. Shen, “Optimal sampling of Gabor features for face recognition”, Pattern Recognition Letters 25 (2004) 267–276.

[22]J. Cook, C. Mccool, V. Chandran, S. Sridharan, “Combined 2D/3D face recognition using log-Gabor templates”, in: IEEE Conference on Video and Signal Based Surveillance, 2006, pp. 83–90.

[23]J. Jones, L. Palmer, “An evaluation of the two-dimensional Gabor filter model of simple receptive fields in cat striate cortex”, Journal of Neurophysiology (1987) 1233-1258.

[24]Bock, H. H. Diday E. (Eds) : “Analysis of Symbolic Data”, Springer Verlag (2000).

[25]Carlo N. Lauro and Francesco Palumbo, “Principal Component Analysis of Interval Data: a Symbolic Data Analysis Approach”, Computational Statistics, Vol.15 n.1 (2000) pp.73-87.

[26]P. S. Hiremath and C. J. Prabhakar, “Face Recognition Technique Using Symbolic LDA Method”, PReMI, LNCS 3776, Springer-Verlang Berlin Hiedelberg, pp.266-271 (2005).

[27]Yoav Freund and Robert E. Schapire, “A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting”, Journal Of Computer And System Sciences, Vol. 55,pp-119-139 (1997).

[28]I.A. Kakadiaris, G. Passalis, G. Toderici, N. Murtuza, Y. Lu, N. Karampatziakis, T. Theoharis, “3D face recognition in the presence of facial expressions: an annotated deformable model approach”, IEEE Transactions on Pattern Analysis and Machine Intelligence 29 (4) (2007) 640–649.

[29]M. H. usken, M. Brauckmann, S. Gehlen, C. Malsburg, “Strategies and benefits of fusion of 2D and 3D face recognition”, in: IEEE Workshop on Face Recognition Grand Challenge Experiments, 2005, pp. 174–181.

[30]A.S. Mian, M. Bennamoun, R. Owens, “An efficient multimodal 2D–3D hybridapproach to automatic face recognition”, IEEE Transactions on Pattern Analysisand Machine Intelligence 29 (11) (2007) 1927–1943.