Face Recognition Using Exact Gaussian-Hermit Moments

Face recognition systems have gained more attention during the last decades. Accurate features are the corner stones in these systems where the performance of recognition and classification processes mainly depends on these features. In this chapter, a new method is proposed for a highly accurate face recognition system. Exact Gaussian-Hermit moments (EGHMs) are used to extract the features of face images where the higher order EGHMs are able to capture the higher-order nonlinear features of these images. The rotation, scaling and translation invariants of EGHMs are used to overcome the geometric distortions. The non-negative matrix factorization (NMF) is a popular image representation method that is able to avoid the drawbacks of principle component analysis (PCA) and independent component analysis (ICA) methods and is able to maintain the image variations. The NMF is used to classify the extracted features. The proposed method is assessed using three face datasets, the ORL, Ncku and UMIST which have different characteristics. The experimental results illustrate the high accuracy of the proposed method against other methods.

[1]  H. Sebastian Seung,et al.  Learning the parts of objects by non-negative matrix factorization , 1999, Nature.

[2]  Thomas S. Huang,et al.  Graph Regularized Nonnegative Matrix Factorization for Data Representation. , 2011, IEEE transactions on pattern analysis and machine intelligence.

[3]  Hong Yan,et al.  Locating and extracting the eye in human face images , 1996, Pattern Recognit..

[4]  David J. Kriegman,et al.  Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection , 1996, ECCV.

[5]  Zhigang Luo,et al.  NeNMF: An Optimal Gradient Method for Nonnegative Matrix Factorization , 2012, IEEE Transactions on Signal Processing.

[6]  Roland T. Chin,et al.  On Image Analysis by the Methods of Moments , 1988, IEEE Trans. Pattern Anal. Mach. Intell..

[7]  Azriel Rosenfeld,et al.  Face recognition: A literature survey , 2003, CSUR.

[8]  Baojun Zhao,et al.  Face Recognition Based on PCA and LDA Combination Feature Extraction , 2009, 2009 First International Conference on Information Science and Engineering.

[9]  Indu Chhabra,et al.  Human face recognition through moment descriptors , 2014, 2014 Recent Advances in Engineering and Computational Sciences (RAECS).

[10]  Kazuo Kyuma,et al.  Face Recognition System Using Local Autocorrelations and Multiscale Integration , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[11]  Zhigang Luo,et al.  Manifold Regularized Discriminative Nonnegative Matrix Factorization With Fast Gradient Descent , 2011, IEEE Transactions on Image Processing.

[12]  Karim Faez,et al.  Face recognition using adaptively weighted patch PZM array from a single exemplar image per person , 2008, Pattern Recognit..

[13]  Stan Z. Li,et al.  Learning spatially localized, parts-based representation , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[14]  Xianzhong Long,et al.  Discriminative graph regularized extreme learning machine and its application to face recognition , 2015, Neurocomputing.

[15]  Jan Flusser,et al.  Scale invariants from Gaussian-Hermite moments , 2017, Signal Process..

[16]  Jenn-Jier James Lien,et al.  Facial expression recognition system based on rigid and non-rigid motion separation and 3D pose estimation , 2009, Pattern Recognit..

[17]  C. Singh,et al.  Face recognition using Zernike and complex Zernike moment features , 2011, Pattern Recognition and Image Analysis.

[18]  K. Manikantan,et al.  Face Recognition Using Gabor Filter Based Feature Extraction with Anisotropic Diffusion as a Pre-processing Technique , 2015 .

[19]  Vijay Vaidehi,et al.  An Efficient Face Recognition System Using DWT-ICA Features , 2011, 2011 International Conference on Digital Image Computing: Techniques and Applications.

[20]  Driss Aboutajdine,et al.  Local appearance based face recognition method using block based steerable pyramid transform , 2011, Signal Process..

[21]  Wen Gao,et al.  Local Gabor binary pattern histogram sequence (LGBPHS): a novel non-statistical model for face representation and recognition , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[22]  Jan Flusser,et al.  Rotation and noise invariant near-infrared face recognition by means of Zernike moments and spectral regression discriminant analysis , 2013, J. Electronic Imaging.

[23]  Marian Stewart Bartlett,et al.  Face recognition by independent component analysis , 2002, IEEE Trans. Neural Networks.

[24]  Jun Wang,et al.  Face recognition based on pixel-level and feature-level fusion of the top-level's wavelet sub-bands , 2015, Inf. Fusion.

[25]  J. Flusser,et al.  Moments and Moment Invariants in Pattern Recognition , 2009 .

[26]  Alex Pentland,et al.  Face recognition using eigenfaces , 1991, Proceedings. 1991 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[27]  Norbert Krüger,et al.  Face Recognition by Elastic Bunch Graph Matching , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[28]  S. M. Mahbubur Rahman,et al.  Bayesian face recognition using 2D Gaussian-Hermite moments , 2015, EURASIP J. Image Video Process..

[29]  Wenbin Li,et al.  Graph regularized discriminative non-negative matrix factorization for face recognition , 2013, Multimedia Tools and Applications.

[30]  Xin Liu,et al.  Document clustering based on non-negative matrix factorization , 2003, SIGIR.

[31]  Zhenmin Tang,et al.  Feature extraction using local structure preserving discriminant analysis , 2014, Neurocomputing.

[32]  Zhong Jin,et al.  Face recognition using discriminant sparsity neighborhood preserving embedding , 2012, Knowl. Based Syst..

[33]  Moataz M. Abdelwahab,et al.  Face and gesture recognition for human computer interaction employing 2DHoG , 2013, 2013 IEEE 56th International Midwest Symposium on Circuits and Systems (MWSCAS).

[34]  Pierre Comon,et al.  Independent component analysis, A new concept? , 1994, Signal Process..

[35]  Jun Wang,et al.  An improved LBP algorithm for texture and face classification , 2014 .

[36]  Khalid M. Hosny,et al.  Fast computation of accurate Gaussian-Hermite moments for image processing applications , 2012, Digit. Signal Process..

[37]  Yulian Zhu,et al.  Semi-random subspace method for face recognition , 2009, Image Vis. Comput..

[38]  Nancy Bertin,et al.  Nonnegative Matrix Factorization with the Itakura-Saito Divergence: With Application to Music Analysis , 2009, Neural Computation.

[39]  Arun Ross,et al.  An introduction to biometric recognition , 2004, IEEE Transactions on Circuits and Systems for Video Technology.

[40]  Urbano Nunes,et al.  Trainable classifier-fusion schemes: An application to pedestrian detection , 2009, 2009 12th International IEEE Conference on Intelligent Transportation Systems.

[41]  Q. M. Jonathan Wu,et al.  Curvelet based face recognition via dimension reduction , 2009, Signal Process..

[42]  Zahir M. Hussain,et al.  Higher order orthogonal moments for invariant facial expression recognition , 2010, Digit. Signal Process..

[43]  Huorong Ren,et al.  Nonparametric subspace analysis fused to 2DPCA for face recognition , 2014 .

[44]  D. B. Graham,et al.  Characterising Virtual Eigensignatures for General Purpose Face Recognition , 1998 .

[45]  Ekta Walia,et al.  Rotation invariant complex Zernike moments features and their applications to human face and character recognition , 2011 .

[46]  Andy Harter,et al.  Parameterisation of a stochastic model for human face identification , 1994, Proceedings of 1994 IEEE Workshop on Applications of Computer Vision.

[47]  Penio S. Penev,et al.  Local feature analysis: A general statistical theory for object representation , 1996 .

[48]  Timothy F. Cootes,et al.  Automatic Interpretation and Coding of Face Images Using Flexible Models , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[49]  Lei Yu,et al.  Gabor texture representation method for face recognition using the Gamma and generalized Gaussian models , 2010, Image Vis. Comput..

[50]  Jongsun Kim,et al.  Effective representation using ICA for face recognition robust to local distortion and partial occlusion , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[51]  Pengfei Shi,et al.  Face recognition using difference vector plus KPCA , 2012, Digit. Signal Process..

[52]  Guohong Huang,et al.  Fusion (2D)2PCALDA: A new method for face recognition , 2010, Appl. Math. Comput..

[53]  Norbert Krüger,et al.  Face recognition by elastic bunch graph matching , 1997, Proceedings of International Conference on Image Processing.

[54]  Bo Yang,et al.  Design of high-order rotation invariants from Gaussian-Hermite moments , 2015, Signal Process..

[55]  Timo Ahonen,et al.  Recognition of blurred faces using Local Phase Quantization , 2008, 2008 19th International Conference on Pattern Recognition.