Multiway analysis for face recognition

The benefit of multiway analysis is it can extract underlying structures, as well as, establishing unknown relationship among the components (modes) of multidimensional data. This can be achieved using tensor decomposition approach. The aim of this paper is to emphasize on the advantage of this approach in face applications. As face researches have demonstrated that human faces are represented in high dimensional space with static and dynamic facial attributes, a good interpretation of a model can thus reveal an explicit relationship between the model and the factors. Motivated by this, we study the effect of two facial attributes, expression and intensity towards recognition performance. At the same time, we compare the recognition rates of tensor model with Principal Component Analysis (PCA) technique. The results showed that tensor model gives a higher recognition rate compared to PCA and there is an interaction effect between expression and intensity factors that affect the recognition performance.

[1]  Rasmus Bro,et al.  Multi-way Analysis with Applications in the Chemical Sciences , 2004 .

[2]  David E. Booth,et al.  Multi-Way Analysis: Applications in the Chemical Sciences , 2005, Technometrics.

[3]  Tamara G. Kolda,et al.  Tensor Decompositions and Applications , 2009, SIAM Rev..

[4]  Hui Zhang,et al.  A framework of face synthesis based on multilinear analysis , 2016, VRCAI.

[5]  Junbin Gao,et al.  Tensor Regression Based on Linked Multiway Parameter Analysis , 2014, 2014 IEEE International Conference on Data Mining.

[6]  Demetri Terzopoulos,et al.  Multilinear Analysis of Image Ensembles: TensorFaces , 2002, ECCV.

[7]  L. Tucker,et al.  Some mathematical notes on three-mode factor analysis , 1966, Psychometrika.

[8]  Michael G. Strintzis,et al.  Face Recognition , 2008, Encyclopedia of Multimedia.

[9]  Stefanie Rukavina,et al.  Expression intensity, gender and facial emotion recognition: Women recognize only subtle facial emotions better than men. , 2010, Acta psychologica.

[10]  D. Gillies,et al.  Chapter 18: Sub-tensor Decomposition for Expression Variant 3D Faces Recognition , 2008, 2008 3rd International Conference on Geometric Modeling and Imaging.

[11]  David I. Perrett,et al.  The Emotion Recognition Task: A Paradigm to Measure the Perception of Facial Emotional Expressions at Different Intensities , 2007, Perceptual and motor skills.

[12]  Jun Wang,et al.  A 3D facial expression database for facial behavior research , 2006, 7th International Conference on Automatic Face and Gesture Recognition (FGR06).

[13]  Narendra Ahuja,et al.  Compact representation of multidimensional data using tensor rank-one decomposition , 2004, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004..

[14]  Amnon Shashua,et al.  Linear image coding for regression and classification using the tensor-rank principle , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[15]  Hanspeter Pfister,et al.  Face transfer with multilinear models , 2005, ACM Trans. Graph..

[16]  Marek R. Ogiela,et al.  Multimedia tools and applications , 2005, Multimedia Tools and Applications.

[17]  Manop Phankokkruad,et al.  Effect of variation factors on the processing time of the face recognition algorithms in video sequence , 2016, ICIIP.

[18]  Demetri Terzopoulos,et al.  Multilinear image analysis for facial recognition , 2002, Object recognition supported by user interaction for service robots.

[19]  Mohammed Bennamoun,et al.  1D-PCA, 2D-PCA to nD-PCA , 2006, 18th International Conference on Pattern Recognition (ICPR'06).

[20]  Witold Pedrycz,et al.  Face recognition: A study in information fusion using fuzzy integral , 2005, Pattern Recognit. Lett..

[21]  Pieter M. Kroonenberg,et al.  Three-mode principal component analysis : theory and applications , 1983 .

[22]  Ying-Ke Lei,et al.  Maximum margin criterion with tensor representation , 2010, Neurocomputing.

[23]  Gregory Shakhnarovich,et al.  Face Recognition in Subspaces , 2011, Handbook of Face Recognition.

[24]  Jieping Ye,et al.  Generalized Low Rank Approximations of Matrices , 2004, Machine Learning.

[25]  L Sirovich,et al.  Low-dimensional procedure for the characterization of human faces. , 1987, Journal of the Optical Society of America. A, Optics and image science.

[26]  Heng Tao Shen,et al.  Principal Component Analysis , 2009, Encyclopedia of Biometrics.