A Comparative Study on Subspace Methods for Face Recognition under Varying Facial Expressions

Face recognition is one of the widely used research topic in biometric fields and it is rigorously studied. Recognizing faces under varying facial expressions is still a very challenging task because adjoining of real time expression in a person face causes a wide range of difficulties in recognition systems. Moreover facial expression is a way of nonverbal communication. Facial expression will reveal the sensation or passion of a person and also it can be used to reveal someone’s mental views and psychosomatic aspects. Subspace analysis are the most vital techniques which are used to find the basis vectors that optimally cluster the projected data according to their class labels. Subspace is a subset of a larger space, which contains the properties of the larger space. The key contribution of this article is, we have developed and analyzed the 2 state of the art subspace approaches for recognizing faces under varying facial expressions using a common set of train and test images. This evaluation gives us the exact face recognition rates of the 2 systems under varying facial expressions. This exhaustive analysis would be a great asset for researchers working world-wide on face recognition under varying facial expressions. The train and test images are considered from standard public face databases ATT, and JAFFE.

[1]  Alistair G. Rust,et al.  Image redundancy reduction for neural network classification using discrete cosine transforms , 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium.

[2]  Matti Pietikäinen,et al.  Face Description with Local Binary Patterns: Application to Face Recognition , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[3]  Mohammed Bennamoun,et al.  Linear Regression for Face Recognition , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[4]  Allen Y. Yang,et al.  Robust Face Recognition via Sparse Representation , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

[6]  Martin D. Levine,et al.  Face Recognition Using the Discrete Cosine Transform , 2001, International Journal of Computer Vision.

[7]  Horst Bischof,et al.  Robust Recognition Using Eigenimages , 2000, Comput. Vis. Image Underst..

[8]  Meng Joo Er,et al.  Face recognition with radial basis function (RBF) neural networks , 2002, IEEE Trans. Neural Networks.

[9]  Shang-Hong Lai,et al.  Face Verification With Local Sparse Representation , 2013, IEEE Signal Processing Letters.

[10]  Fan Yang,et al.  Implementation of an RBF neural network on embedded systems: real-time face tracking and identity verification , 2003, IEEE Trans. Neural Networks.

[11]  Thomas Deselaers,et al.  ClassCut for Unsupervised Class Segmentation , 2010, ECCV.

[12]  G. Josemin Bala,et al.  A comparative study on ICA and LPP based Face Recognition under varying illuminations and facial expressions , 2013, 2013 International Conference on Signal Processing , Image Processing & Pattern Recognition.

[13]  Wen Gao,et al.  Histogram of Gabor Phase Patterns (HGPP): A Novel Object Representation Approach for Face Recognition , 2007, IEEE Transactions on Image Processing.

[14]  Antonio Albiol,et al.  Face recognition using HOG-EBGM , 2008, Pattern Recognit. Lett..

[15]  John Wright,et al.  Dense Error Correction Via $\ell^1$-Minimization , 2010, IEEE Transactions on Information Theory.

[16]  Shree K. Nayar,et al.  Attribute and simile classifiers for face verification , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[17]  Tal Hassner,et al.  Effective Unconstrained Face Recognition by Combining Multiple Descriptors and Learned Background Statistics , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[18]  Ran He,et al.  Two-Stage Nonnegative Sparse Representation for Large-Scale Face Recognition , 2013, IEEE Transactions on Neural Networks and Learning Systems.

[19]  Joachim M. Buhmann,et al.  Distortion Invariant Object Recognition in the Dynamic Link Architecture , 1993, IEEE Trans. Computers.

[20]  Allen Y. Yang,et al.  Fast L1-Minimization Algorithms For Robust Face Recognition , 2010 .

[21]  Roberto Brunelli,et al.  Face Recognition: Features Versus Templates , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

[22]  Rama Chellappa,et al.  Video-based face recognition via joint sparse representation , 2013, 2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG).

[23]  Lei Zhang,et al.  Gabor Feature Based Sparse Representation for Face Recognition with Gabor Occlusion Dictionary , 2010, ECCV.

[24]  G. Josemin Bala,et al.  Time Complexity for Face Recognition under varying Pose, Illumination and Facial Expressions based on Sparse Representation , 2012 .

[25]  Jian Yang,et al.  Sparse Representation Classifier Steered Discriminative Projection With Applications to Face Recognition , 2013, IEEE Transactions on Neural Networks and Learning Systems.

[26]  Vinay Bettadapura,et al.  Face Expression Recognition and Analysis: The State of the Art , 2012, ArXiv.

[27]  Witold Pedrycz,et al.  Conditional fuzzy clustering in the design of radial basis function neural networks , 1998, IEEE Trans. Neural Networks.

[28]  Rama Chellappa,et al.  A feature based approach to face recognition , 1992, Proceedings 1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.