The facial expression recognition method of random forest based on improved PCA extracting feature

In this paper, we propose a new framework for facial expression classification. This framework utilizes random forest as the classifier based on the features extracted from improved principal component analysis (PCA). Traditional PCA has two drawbacks: it is difficult to estimate the covariance matrix, and it is computational prohibitive to get the eigenvectors. In order to solve the two problems, we adopt an improved PCA to extract the features and uses the random forest algorithm as the classifier. The experiments are conducted based on the JAFFE facial expressions library, and are compared with the support vector machine (SVM) classifier. Theoretical analysis and experimental results show that the facial expression recognition system of random forest based on improved PCA extracting feature, whether in terms of facial expression recognition rate or running speed, has shown significant improvement.

[1]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[2]  David J. Kriegman,et al.  From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[3]  Terence Sim,et al.  The CMU Pose, Illumination, and Expression Database , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[4]  Muhammad Sharif,et al.  A survey: face recognition techniques under partial occlusion , 2014, Int. Arab J. Inf. Technol..

[5]  Iván Gómez-Conde,et al.  Simple Human Gesture Detection and Recognition Using a Feature Vector and a Real-Time Histogram Based Algorithm , 2011 .

[6]  J. Daugman.  Uncertainty relation for resolution in space, spatial frequency, and orientation optimized by two-dimensional visual cortical filters. , 1985, Journal of the Optical Society of America. A, Optics and image science.

[7]  Anil K. Jain,et al.  Unsupervised Learning of Finite Mixture Models , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[8]  Luc Van Gool,et al.  Real-time facial feature detection using conditional regression forests , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[9]  Kun Zhang,et al.  Towards Improving Social Communication Skills With Multimodal Sensory Information , 2014, IEEE Transactions on Industrial Informatics.

[10]  Jean-Michel Poggi,et al.  Variable selection using random forests , 2010, Pattern Recognit. Lett..

[11]  B. Ripley,et al.  Pattern Recognition , 1968, Nature.

[12]  Pawan Sinha,et al.  Face Recognition by Humans: Nineteen Results All Computer Vision Researchers Should Know About , 2006, Proceedings of the IEEE.

[13]  Luc Van Gool,et al.  Real time head pose estimation with random regression forests , 2011, CVPR 2011.

[14]  Alejandro F. Frangi,et al.  Two-dimensional PCA: a new approach to appearance-based face representation and recognition , 2004 .

[15]  David J. Kriegman,et al.  Localizing parts of faces using a consensus of exemplars , 2011, CVPR.

[16]  Abhishek Kumar Srivastava,et al.  Performance Improvements in Face Classification using Random Forest , 2012 .

[17]  Jian Yang,et al.  From image vector to matrix: a straightforward image projection technique - IMPCA vs. PCA , 2002, Pattern Recognit..

[18]  Luc Van Gool,et al.  Random Forests for Real Time 3D Face Analysis , 2012, International Journal of Computer Vision.

[19]  Peng Cui,et al.  A SVM-Based Feature Extraction for Face Recognition , 2016, ICYCSEE.

[20]  Paul A. Griffin,et al.  Statistical Approach to Shape from Shading: Reconstruction of Three-Dimensional Face Surfaces from Single Two-Dimensional Images , 1996, Neural Computation.

[21]  Meng Joo Er,et al.  Illumination compensation and normalization for robust face recognition using discrete cosine transform in logarithm domain , 2006, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[22]  Youn Jung Park,et al.  SVM-based feature extraction for face recognition , 2010, Pattern Recognit..

[23]  Tao Gong,et al.  High-precision Immune Computation for Secure Face Recognition , 2012 .

[24]  Moustapha Kardouchi,et al.  Fast and efficient face recognition system using random forest and histograms of oriented gradients , 2012, 2012 BIOSIG - Proceedings of the International Conference of Biometrics Special Interest Group (BIOSIG).

[25]  Zoubin Ghahramani,et al.  A Very Simple Safe-Bayesian Random Forest , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[26]  S. Mohamed Mansoor Roomi,et al.  A Review of Face Recognition Methods , 2013, Int. J. Pattern Recognit. Artif. Intell..

[27]  Ahmet Sertbas,et al.  Evaluation of face recognition techniques using PCA, wavelets and SVM , 2010, Expert Syst. Appl..

[28]  P. Srinivasa Rao,et al.  Image Segmentation Method Based On Finite Doubly Truncated Bivariate Gaussian Mixture Model with Hierarchical Clustering , 2011 .