ABSTRACT The purpose of this study is to enhance the algorithms towards the development of an efficient three dimensional face recognition system in the presence of expressions. The overall aim is to analyse patterns of expressions based on techniques relating to feature distances compare to the benchmarks. To investigate how the use of distances can help the recognition process, a feature set of diagonal distance patterns, were determined and extracted to distinguish face models. The significant finding is that, to solve the problem arising from data with facial expressions, the feature sets of the expression-invariant and expression-variant regions were determined and described by geodesic distances and Euclidean distances. By using regression models, the correlations between expressions and neutral feature sets were identified. The results of the study have indicated that our proposed analysis methods of facial expressions, was capable of undertaking face recognition using a minimum set of features improving efficiency and computation.
Cite this paper
X. Han, M. Yap and I. Palmer, "Face Recognition in the Presence of Expressions," Journal of Software Engineering and Applications, Vol. 5 No. 5, 2012, pp. 321-329. doi: 10.4236/jsea.2012.55038.
 Y. Wang, C. Chua and Y. Ho, “Facial Appearance Detection and Face Recognition from 2D and 3D Images,” Pattern Recognition Letters, Vol. 23, No. 10, 2001, pp. 1191-1202.
 X. Han, H. Ugail and I. Palmer, “Method of Characterising 3D Faces Using Gaussian Curvature,” Chinese Conference on Pattern Recognition, Nanjing, 4-6 November 2009, pp. 528-532.
 B. Amberg, R. Knothe and T. Vetter, “Expression Invariant 3D Face Recognition with a Morphable Model,” IEEE International Conference on Automatic Face and Gesture Recognition, 2008, pp. 1-6.
 N. Alyuz, B. Gokberk, H. Dibeklioglu and L. Akarun, “Component-Based Registration with Curvature Descriptors for Expression Insensitive 3D Face Recognition,” IEEE International Conference on Automatic Face & Gesture Recognition, Amsterdam, 17-19 September 2008, pp. 1-6.
 D. Smeets, T. Fabry, J. Hermans, D. Vandermeulen and 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,” The 20th International Conference on Pattern Recognition, Istanbul, 23-26 August 2010, pp. 1172-1175. doi:10.1109/ICPR.2010.293
 Y. Wang, G. Pan and Z. Wu, “3D Face Recognition in the Presence of Expression: A Guidance-Based Constraint Deformation Approach,” IEEE Conference on Computer Vision and Pattern Recognition, 2007, pp. 1180-1187.
 X. Lu and A. K. Jain, “Deformation Modeling for Robust 3D Face Matching,” IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Vol. 2, 2006, pp. 1377-1383. doi:10.1109/CVPR.2006.96
 I. A. Kakadiaris, G. Passalis, G. Toderici, M. N. Murtuza, Y. Lu, N. Karampatziakis and T. Theoharis, “Three-Dimensional Face Recognition in the Presence of Facial Expressions: An Annotated Deformable Model Approach,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 29, No. 4, 2007, pp. 640-649.
 H. Lee and D. Kim, “Expression-invariant Face Recognition by Facial Expression Transformations “ Pattern Recognition Letters, Vol. 29, No. 13, 2008, pp. 1797-1805.
 F. R. Al-Osaimi, M. Bennamoun and A. Mian, “On Decomposing an Unseen 3D Face into Neutral Face and Expression Deformations,” Advances in Biometrics, Vol. 5558, 2009, pp. 22-31. doi:10.1007/978-3-642-01793-3_3
 X. Li, T. Jia and H. Zhang, “Expression-insensitive 3D Face Recognition Using Sparse Representation,” IEEE Conference on Computer Vision and Pattern Recognition, 2009, pp. 2575-2582.
 A. B. Moreno and A. Sanchez, “GavabDB: A 3D Face Database,” Proceedings 2nd COST Workshop on Biometrics on the Internet: Fundamentals, Advances and Applications, Vigo, 25-26 March 2004, pp. 77-82.
 L. J. Yin, X. Z. Wei, Y. Sun, J. Wang and M. J. Rosato, “A 3D Facial Expression Database for Facial Behavior Re- search,” International Conference on Automatic Face and Gesture Recognition, Southampton, 2-6 April 2006, pp. 211-216. doi:10.1109/FGR.2006.6
 A. M. Bronstein, M. M. Bronstein and R. Kimmel, “Robust Expression-Invariant Face Recognition from Partially Missing Data,” European Conference on Computer Vision, Vol. 3953, 2006, pp. 396-408.
 A. M. Bronstein, M. M. Bronstein and R. Kimmel, “Three-Dimensional Face Recognition,” International Journal of Computer Vision, Vol. 64, No. 1, 2005, pp. 5-30.
 A. M. Bronstein, M. M. Bronstein and R. Kimmel, “Expression-invariant 3D Face Recognition,” International Conference on Audio- and Video-based Biometric Person Authentication, Guildford, 9-11 June 2003, pp. 62-69.
 K. I. Chang, K. W. Bowyer and P. J. Flynn, “Multiple Nose Region Matching for 3D Face Recognition under Varying Facial Expression,” IEEE Transaction on Pattern Analysis and Machine Intelligence, Vol. 28, No. 10, 2006, pp. 1695-1700. doi:10.1109/TPAMI.2006.210
 T. Faltemier, K. W. Bowyer and P. Flynn, “A Region Ensemble for 3D Face Recognition,” IEEE Transactions on Information Forensics and Security, Vol. 3, No. 1, 2008, pp. 62-73. doi:10.1109/TIFS.2007.916287
 K. Fatimah, N. Khalid and A. Lili, “3D Face Recognition Using Multiple Features for Local Depth Information,” International Journal of Computer Science and Network Security, Vol. 9, No. 1, 2009, pp. 27-32.
 S. Berretti, A. Del Bimbo and P. Pala, “3D Face Recognition Using Isogeodesic Stripes,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 32, No. 12, 2010, pp. 2162-2177. doi:10.1109/TPAMI.2010.43
 C. Samir, A. Srivastava and M. Daoudi, “3D Face Recognition Using Shapes of Facial Curves,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 28, No. 11, 2006, pp. 1858-1863.
 X. Chai, S. Shan, X. Chen and W. Gao, “Locally Linear Regression for Pose-Invariant Face Recognition,” IEEE Transactions on Image Processing, Vol. 16, No. 7, 2007, pp. 1716-1725. doi:10.1109/TIP.2007.899195
 I. Naseem, R. Togneri and M. Bennamoun, “Linear Regression for Face Recognition,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 32, No. 11, 2010, pp. 2106-2112. doi:10.1109/TPAMI.2010.128
 R. D. Tobias, “An Introduction to Partial Least Squares Regression,” SUGI Proceedings, Orlando 2-5 April 1995, 1995, pp. 1-8.
 M. Tranmer and M. Elliot, “Multiple Linear Regression,” The Cathie Marsh Centre for Census and Survey Research (CCSR) 2008.
 O. Gervei, A. Ayatollahi and N. Gervei, “3D Face Recognition Using Modified PCA Methods,” World Academy of Science, Engineering and Technology, No. 39, 2010, pp. 264-267.
 X. Li and F. Da, “3D Face Recognition by Deforming the Normal Face,” International Conference on Pattern Recognition, Istanbul, 23-26 August 2010, pp. 3975-3978.
 M. H. Mahoor and M. Abdel-Mottaleb, “3D Face Recognition Based on 3D Ridge Lines in Range Data,” IEEE International Conference on Image Processing, Vol. 1, 2007, pp. 137-140. doi:10.1109/ICIP.2007.437891