Conditional convolution neural network enhanced random forest for facial expression recognition

Zhang, 2016, A deep neural network-driven feature learning method for multi-view facial expression recognition, IEEE Trans. Multimed., 18, 2528, 10.1109/TMM.2016.2598092 Yuan, 2018, A regularized ensemble framework of deep learning for cancer detection from multi-class, imbalanced training data, Pattern Recognit., 77, 160, 10.1016/j.patcog.2017.12.017 Fang, 2018, Personality driven task allocation for emotional robot team, Int. J. Mach. Learn. Cybern., inpress Lopes, 2017, Facial expression recognition with convolutional neural networks: coping with few data and the training sample order, Pattern Recognit., 61, 610, 10.1016/j.patcog.2016.07.026 Hamouda, 2016, Optimizing discriminability of globally Binarized face templates, Arab. J. Sci. Eng., 41, 2837, 10.1007/s13369-015-2020-3 Jung, 2015, Joint fine-tuning in deep neural networks for facial expression recognition, 2983 Tawari, 2013, Face expression recognition by cross modal data association, IEEE Trans. Multimed., 15, 1543, 10.1109/TMM.2013.2266635 O. Rudovic, I. Patras, M. Pantic, Coupled gaussian process regression for pose-invariant facial expression recognition, in: Proceedings of the Computer Vision–ECCV (2010) 350–363. Moore, 2011, Local binary patterns for multi-view facial expression recognition, Comput. Vis. Image Underst., 115, 541, 10.1016/j.cviu.2010.12.001 X. Yuan, M. Abouelenien, M. Elhoseny, Quantum Computing:An Environment for Intelligent Large Scale Real Application, Springer International Publishing, pp. 433–448. Shan, 2012, Smile detection by boosting pixel differences, IEEE Trans. Image Process., 21, 431, 10.1109/TIP.2011.2161587 Nguyen, 2015, Dash-n: joint hierarchical domain adaptation and feature learning, IEEE Trans. Image Process., 24, 5479, 10.1109/TIP.2015.2479405 Xu, 2015, Facial expression recognition based on transfer learning from deep convolutional networks, 702 Donahue, 2014, Decaf: a deep convolutional activation feature for generic visual recognition., 32, 647 Girshick, 2015, Fast R-CNN, 1440 Jia, 2014, Caffe: convolutional architecture for fast feature embedding, 675 Parkhi, 2015, Deep face recognition., 1, 6 Mollahosseini, 2016, Going deeper in facial expression recognition using deep neural networks, 1 Liu, 2014, Deeply learning deformable facial action parts model for dynamic expression analysis, 143 Liu, 2013, Au-aware deep networks for facial expression recognition, 1 Gupta, 2018, Illumination invariants in deep video expression recognition, Pattern Recognit., 76, 25, 10.1016/j.patcog.2017.10.017 Yang, 2017, Joint and collaborative representation with local adaptive convolution feature for face recognition with single sample per person, Pattern Recognit., 66, 117, 10.1016/j.patcog.2016.12.028 Bulo, 2014, Neural decision forests for semantic image labeling, 81 Breiman, 2001, Random forests, Mach. Learn., 45, 5, 10.1023/A:1010933404324 Dantone, 2012, Real-time facial feature detection using conditional regression forests, 2578 Sun, 2012, Conditional regression forests for human pose estimation, 3394 A. Criminisi, J. Shotton, E. Konukoglu, Decision forests for classification, regression, density estimation, manifold learning and semi-supervised learning, Microsoft Research Cambridge, Technical Report MSR-TR-2011-114 5(6) (2011) 12. Fanelli, 2013, Random forests for real time 3d face analysis, Int. J. Comput. Vis., 101, 437, 10.1007/s11263-012-0549-0 Fanelli, 2010, Hough forest-based facial expression recognition from video sequences, 195 Shan, 2009, Facial expression recognition based on local binary patterns: a comprehensive study, Image Vis. Comput., 27, 803, 10.1016/j.imavis.2008.08.005 Kim, 2016, Kernel locality-constrained sparse coding for head pose estimation, IET Comput. Vis., 10, 828, 10.1049/iet-cvi.2015.0242 Ito, 2005, Smile and laughter recognition using speech processing and face recognition from conversation video, 8 Kahou, 2014, Facial expression analysis based on high dimensional binary features, 135 Zheng, 2014, Multi-view facial expression recognition based on group sparse reduced-rank regression, IEEE Trans. Affect. Comput., 5, 71, 10.1109/TAFFC.2014.2304712 A. Dapogny, K. Bailly, S. Dubuisson, Dynamic pose-robust facial expression recognition by multi-view pairwise conditional random forests, arXiv:1607.06250 (2016). Hou, 2012, Image signature: highlighting sparse salient regions, IEEE Trans. Pattern Anal. Mach. Intell., 34, 194, 10.1109/TPAMI.2011.146 Goferman, 2012, Context-aware saliency detection, IEEE Trans. Pattern Anal. Mach. Intell., 34, 1915, 10.1109/TPAMI.2011.272 Liu, 2016, Robust head pose estimation using Dirichlet-tree distribution enhanced random forests, Neurocomputing, 173, 42, 10.1016/j.neucom.2015.03.096 Lucey, 2010, The extended Cohn–Kanade dataset (ck+): a complete dataset for action unit and emotion-specified expression, 94 Lyons, 1999, Automatic classification of single facial images, IEEE Trans. Pattern Anal. Mach. Intell., 21, 1357, 10.1109/34.817413 Yin, 2006, A 3d facial expression database for facial behavior research, 211 Huang, 2007, Labeled faces in the wild: a database for studying face recognition in unconstrained environments Dapogny, 2015, Pairwise conditional random forests for facial expression recognition, 3783 Zhang, 2015, Facial expression recognition using {l} _ {p}-norm MKL multiclass-SVM, Mach. Vis. Appl., 26, 467, 10.1007/s00138-015-0677-y Krizhevsky, 2012, ImageNet classification with deep convolutional neural networks, 1097 K. He, X. Zhang, S. Ren, J. Sun, Deep residual learning for image recognition, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2016) 770–778.