3D Face Recognition using Patch Geodesic Derivative Pattern

Subject Areas : International Journal of Smart Electrical Engineering

Soheila Gheisari ¹ ( Electrical Engineering Department, Central Tehran Branch, Islamic Azad University, Tehran, Iran )
Shahram Javadi ² ( Electrical Engineering Department, Central Tehran Branch, Islamic Azad University, Tehran, Iran )
Alireza Kashaninya ³ ( Electrical Engineering Department, Central Tehran Branch, Islamic Azad University, Tehran, Iran )

Received: 2015-04-13 Accepted : 2015-04-13 Published : 2013-06-01

Keywords: 3D Face, Geodesic Distance, Derivative,

Abstract :

In this paper, a novel Patch Geodesic Derivative Pattern (PGDP) describing the texture map of a face through its shape data is proposed. Geodesic adjusted textures are encoded into derivative patterns for similarity measurement between two 3D images with different pose and expression variations. An extensive experimental investigation is conducted using the publicly available Bosphorus and BU-3DFE databases covering face recognition under pose and expression changes. The performance of the proposed method is compared with the performance of the state-of-the-art benchmark approaches. The encouraging experimental results demonstrate that the proposed method is a new solution for 3D face recognition in single model databases.

References:

[1]

J. Y. Cartoux, J. T. Lapreste, M. Richetin, Face Authentication or Recognition by Profile Extraction from Range Images, Proc. of the Workshop Interpretation of 3D Scenes, pp.194-199, 1989.

[2]

G. G. Gordon, Face Recognition Based on Depth and Curvature Features, Proc. of the IEEE Conference on Computer Vision and Pattern Recognition, pp.108-110, 1992.

[3]

H. T. Tanaka, M. Ikeda, H. Chiaki, Curvature-Based Face Surface Recognition Using Spherical Correlation-Principal Directions for Curved Object Recognition, Proc. of the 3rd. IEEE International Conference on Face and Gesture Recognition, pp.372-377, 1998.

[4]

C. Chua, F. Han, Y. Ho, 3D Human Face Recognition Using Point Signature, Proc. of the 4th IEEE International Conference on Automatic Face and Gesture Recognition, pp.233-238, 2000.

[5]

C. Hesher, A. Srivastava, G. Erlebacher, Principal Component Analysis of Range Images for Facial Recognition, Proc. of the International Conference on Imaging Science, Systems, and Technology, pp.62-68, 2002.

[6]

G. Pan, Z. Wu, Y. Pan, Automatic 3D Face Verification from Range Data, Proc. of the IEEE International Conference on Acoustics, Speech, and Signal Processing, pp.193-196, 2003.

[7]

S. D. Chang, M. Rioux, J. Domey, Face recognition with range images and intensity images, Optical Engineering, Vol.36, No.4, pp.1106-1112, 1997.

 

[8]

X. Lu, A. K. Jain, D. Colbry, Matching 2.5D Face Scans to 3D Models, IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol.28, No.1, pp.31-43, 2006.

[9]

A. M. Bronstein, M. M. Bronstein, R. Kimmel, Expression-Invariant Representations of Faces, IEEE Trans. on Image Processing, Vol.16, No.1, pp.188-197, 2007.

[10]

A. S. Mian, M. Bennamoun, R. Owens, An efficient multimodal 2D-3D hybrid approach to automatic face recognition, IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol.29, No.11, pp.1927-1943, 2007.

[11]

S. Malassiotis, M. G. Strintzis, Robust Face Recognition Using 2D and 3D Data: Pose and Illumination Compensation, Pattern Recognition, Vol.38, No.12, pp.2537-2548, 2005.

[12]

L. R. Rabiner, Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition, Proc. IEEE, Vol.77, No.2, pp.257-286, 1989.

[13]

F. Hajati, A. A. Raie, Y. Gao, 2.5D Face Recognition using Patched Geodesic Moments, Pattern Recognition, Vol.45, No.3, pp.969-982, 2012.

[14]

A. Savran, N. Alyuz, H. Dibeklioglu, O. Celiktutan, B. Gokberk, B. Sankur, L. Akarun, Bosphorus Database for 3D Face Analysis, Proc. of the First COST 2101 Workshop on Biometrics and Identity Management (BIOID), pp.47-56, 2008.

[15]

L. Yin, X. Wei, Y. Sun, J. Wang, M. J. Rosato, A 3D Facial Expression Database for Facial Behavior Research, Proc. of the 7th International Conference on Automatic Face and Gesture Recognition (FGR), pp.211-216, 2006.

[16]

T. Ahonen, A. Hadid, M. Pietikainen, Face Description with Local Binary Patterns: Application to Face Recognition, IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol.28, No.12, pp.2037-2041, 2006.

[17]

Y. Lei, M. Bennamoun, A. A. El-Sallam, An efficient 3D face recognition approach based on the fusion of novel local low-level features, Pattern Recognition, Vol.46, No.1, pp.24-37, 2013.

[18]

D. Smeets, T. Fabry, J. Hermans, D. Vandermeulen, P. Suetens, Fusion of an Isometric Deformation Modeling Approach Using Spectral Decomposition and a Region-Based Approach Using ICP for Expression-Invariant 3D Face Recognition, Proceedings of the 2010 20th International Conference on Pattern Recognition, pp.1172-1175, 2010.

[19]

I. Mpiperis, S. Malassiotis, M. G. Strintzis, Expression-Compensated 3D Face Recognition with Geodesically Aligned Bilinear Models, Biometrics: Theory, Applications and Systems, 2008. BTAS 2008. 2nd IEEE International Conference on, pp.1-6, 2008.

[20]

V. D. Kaushik, A. Budhwar, A. Dubey, R. Agrawal, S. Gupta, V. K. Pathak, P. Gupta, An Efficient 3D Face Recognition Algorithm, New Technologies, Mobility and Security (NTMS), 2009 3rd International Conference on, pp.1-5, 2009.