Expression recognition using semantic information and local texture features

In this paper, the cross-database facial expression recognition problem is studied. First, Gaussian Mixture Model is used to improve the facial landmark detection. Second, the local image features are used to model the facial actions. Deep Neural Network is used to represent the low level data variance. Third, the top level expression recognition rules are used in a Fuzzy Inference System to improve the cross-database performance by 11.6 percent in average. Experimental results show that the expression recognition rate is improved constantly over six emotion types compared with traditional Support Vector Machines and Neural Network classifiers . The averaged improvement against Support Vector Machines is 15.1 percent and the averaged improvement against neural network is 8.1 percent.

[1]  Qiang Ji,et al.  Facial Action Unit Recognition by Exploiting Their Dynamic and Semantic Relationships , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  Je-Chang Jeong,et al.  Efficient Deep Neural Network for Digital Image Compression Employing Rectified Linear Neurons , 2016, J. Sensors.

[3]  Margaret Lech,et al.  Averaged Gabor Filter Features for Facial Expression Recognition , 2008, 2008 Digital Image Computing: Techniques and Applications.

[4]  Ioannis Pitas,et al.  The eNTERFACE’05 Audio-Visual Emotion Database , 2006, 22nd International Conference on Data Engineering Workshops (ICDEW'06).

[5]  Ruiping Wang,et al.  Learning Expressionlets via Universal Manifold Model for Dynamic Facial Expression Recognition. , 2016, IEEE transactions on image processing : a publication of the IEEE Signal Processing Society.

[6]  Geoffrey E. Hinton.  Training Products of Experts by Minimizing Contrastive Divergence , 2002, Neural Computation.

[7]  Hong Yan,et al.  Modeling and animation of human expressions using NURBS curves based on facial anatomy , 2002, Signal Process. Image Commun..

[8]  Douglas R. Smith,et al.  Top-Down Synthesis of Divide-and-Conquer Algorithms , 1985, Artif. Intell..

[9]  Jechang Jeong,et al.  Exploiting deep neural networks for digital image compression , 2015, 2015 2nd World Symposium on Web Applications and Networking (WSWAN).

[10]  Wei Zhang,et al.  EM algorithms of Gaussian mixture model and hidden Markov model , 2001, Proceedings 2001 International Conference on Image Processing (Cat. No.01CH37205).

[11]  Beat Fasel,et al.  Automati Fa ial Expression Analysis: A Survey , 1999 .

[12]  William J. Christmas,et al.  Cascaded Collaborative Regression for Robust Facial Landmark Detection Trained Using a Mixture of Synthetic and Real Images With Dynamic Weighting , 2015, IEEE Transactions on Image Processing.

[13]  Cha Zhang,et al.  Image based Static Facial Expression Recognition with Multiple Deep Network Learning , 2015, ICMI.

[14]  Keith Waters,et al.  A muscle model for animation three-dimensional facial expression , 1987, SIGGRAPH.

[15]  Lifang Wu,et al.  An Effective Face Verification Algorithm to Fuse Complete Features in Convolutional Neural Network , 2016, MMM.

[16]  Maja Pantic,et al.  Discriminative Shared Gaussian Processes for Multiview and View-Invariant Facial Expression Recognition , 2015, IEEE Transactions on Image Processing.

[17]  Lijun Yin,et al.  FERA 2015 - second Facial Expression Recognition and Analysis challenge , 2015, 2015 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG).

[18]  Jun Ou,et al.  Automatic Facial Expression Recognition Using Gabor Filter and Expression Analysis , 2010, 2010 Second International Conference on Computer Modeling and Simulation.

[19]  Yee Whye Teh,et al.  A Fast Learning Algorithm for Deep Belief Nets , 2006, Neural Computation.

[20]  Michel F. Valstar,et al.  L2, 1-based regression and prediction accumulation across views for robust facial landmark detection , 2016, Image Vis. Comput..

[21]  Aurobinda Routray,et al.  Automatic facial expression recognition using features of salient facial patches , 2015, IEEE Transactions on Affective Computing.

[22]  William E. Higgins,et al.  Efficient Gabor filter design for texture segmentation , 1996, Pattern Recognit..

[23]  Du-Sik Park,et al.  Rotating your face using multi-task deep neural network , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).