找到一個”好的”訊號表示方式，讓其可以更精準的表達訊號本身的結構、樣式與訊號間的關係一直都是研究人員致力研究的主題。近年來，稀疏編碼技術(sparse coding)在擷取訊號特性的優越性因而越來越受到矚目。經由更進一步的考慮群體特性，稀疏編碼技術在群體階層上產生良好的編碼結果。然而，個別的物體或是動作通常包含許多的資料屬性來描述物體或動作的特性。以動作辨識而言，動作可能包含了不同的視角、姿態與明暗情況。而傳統的稀疏編碼技術無法充分的利用這些特性來獲得更好的效能。
因此，在這篇論文中，我們提出多屬性稀疏編碼技術，透過資料屬性的限制產生更好的結果來對具有多屬性的動作與人臉表情進行辨識。對動作辨識而言，我們首先執行一個以過度分割(over-segmentation)為基礎的背景模型建立與前景切割以獲得人們在執行動作的外型輪廓。接著我們計算多區間的運動歷史影像(motion histogram image, MHI)來表達動作過程中的變化。而具有多屬性的動作可以用多個個別的屬性矩陣來描述這些屬性。這些屬性矩陣之後被整合到稀疏編碼的l_1最佳化式子中。透過這些資料屬性的矩陣，在選擇表示資料的基底時限制與強迫從具有相同屬性的群體中選出，藉以獲得更有效的資料表示結果。特別的是，我們的方法也適用在當訓練資料中只有少部分的資料是知道屬性的狀況(partially label)。
此外，我們進一步的延伸多屬性稀疏編碼技術，結合人臉上的動作單元(Action Units)來進行人臉表情辨識。動作單元不僅可以被表示為個別的屬性矩陣來描述人臉表情的群體特性，也可以做為一個在挑選基底時的限制，因為相同的人臉表情應該會具有非常類似的動作單元組合。而這些群體的限制與動作單元組合相似度的限制，都一起被整合到稀疏編碼的l_1最佳化式子中來辨識人臉表情。
我們透過實驗在多個不同的公開多視角人體動作資料庫與人臉表情資料庫來展示我們方法的有效性與強健性，並獲得了很好的結果。

Sparse coding technique has been proved to be very effective in extracting global features from signals for several different applications. Furthermore, the sparse representation was designed to produce sparse solution at the group level by considering group structure of training images. However, distinctive objects or different action videos usually contain multiple data attributes which are high-level descriptions about the properties of objects or actions. For the action recognition problem, action video may contain multiple attributes, such as different types of viewing angle, pose and illumination. Such multi-attribute properties cannot be fully exploited by the group lasso method since it is not designed to handle multiple attributes.
In this thesis, we propose multi-attribute sparse representation based method enforced with group constraint for the action recognition and facial expression recognition problems which contain multiple data attributes. For the action recognition problem, an over-segmentation based background modeling and foreground detection approach is employed to extract silhouettes from action videos firstly. Then, multiple time intervals of the motion history images are computed to capture motion and pose information in human activities. Actions with multiple attributes can be represented by individual attribute matrices to describe group property for each action instance. These attribute matrices are incorporated into the formulation of l_1-minimization. The sparsity property as well as the group constraints makes the basis selection in sparse coding more efficient in term of accuracy. Especially, our approach is able to operate under the condition of partially labeled attributes in the training data.
Furthermore, we integrate action units (AUs) information and multi-attribute sparse coding for facial expression recognition. AUs not only can be represented by an individual attribute mask to describe group property for each facial expression video, but also as a constraint to enforce that the same facial expressions should have very similar AUs. The group constraint makes the basis selection in sparse coding more efficient and the AU similarity constraint penalizes selecting the dictionary atoms with distance far away the target instance. These groups constraint and the AU similarity constraint are incorporated into the formulation of l_1-minimization to recognize facial expression.
We will demonstrate the proposed multi-attribute sparse coding based method through experiments on several public multi-view human action datasets and facial expression datasets to show the effectiveness and robustness of the proposed method.

[1] M. Elad and M. Aharon, “Image denoising via learned dictionaries and sparse representation,” IEEE conference on Computer vision and Pattern Recognition, 2006.
[2] J. Mairal, F. Bach, J. Ponce, G. Sapiro, and A. Zisserman, “Discriminative learned dictionaries for local image analysis,” IEEE Conference on Computer Vision and Pattern Recognition, 2008.
[3] J. Mairal, M. Elad, and G. Sapiro, "Sparse Representation for Color Image Restoration," IEEE Transactions on Image Processing, vol.17, no.1, pp.53,69, Jan. 2008
[4] J. Wright, A. Y. Yang, A. Ganesh, S. S. Sastry, and Y. Ma, “Robust face recognition via sparse representation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 31, no. 2, pp. 210–227, 2009.
[5] Q. Qiu, Z. Jiang, and R. Chellappa, “Sparse dictionary-based representation and recognition of action attributes,” IEEE International Conference on Computer Vision, 2011.
[6] J. Yang, K. Yu, Y. Gong, and T. Huang,” Linear spatial pyramid matching using sparse coding for image classification,” IEEE Conference on Computer Vision and Pattern Recognition, 2009.
[7] C.-K. Chiang, T.-F. Su, C. Y, and S.-H. Lai, “Multi-attribute sparse representation with group constraints for face recognition under different variations,” IEEE International Conference on Automatic Face and Gesture Recognition, 2013.
[8] C.-P Wei, Y.-W. Chao, Y.-R. Yeh, Y.-C. F. Wang,”Locality-sensitive dictionary learning for sparse representation based classification,” Pattern Recognition, Vol. 46, pp. 1277–1287, 2013
[9] R. Tibshirani, ”Regression shrinkage and selection via the lasso”, Journal of the Royal Statistical Society, pp. 267-288, 1994.
[10] M.R. Osborne, B. Presnell, and B.A. Turlach, “On the lasso and its dual”, Journal of Computational and Graphical Statistics, pp. 319-337, 1999.
[11] M. Yuan and Y. Lin, “Model selection and estimation in regression with grouped variables,” Journal of the Royal Statistical Society: Series B, vol. 68, no. 1, pp. 49–67, 2006.
[12] Y.-W. Chao, Y.-R. Yeh, Y.-W. Chen, Y.-J. Lee, and Y.-C. Wang, “Locality-constrained group sparse representation for robust face recognition,” IEEE International Conference on Image Processing, 2011.
[13] L. Wang, W. Hu, and D. Suter, “Recent developments in human motion analysis,” Pattern Recognition, vol. 36, no. 3, pp. 585–601, 2003.
[14] A. Veeraraghavan, A. Roy-Chowdhury, and R. Chellappa, “Role of shape and kinematics in human movement analysis,” IEEE Conference on Computer Vision and Pattern Recognition, 2004.
[15] A. Bobick and J. Davis, “The recognition of human movement using temporal templates,” IEEE Transactions on Pattern Analysis and Machine Intelligence(PAMI), vol. 23, no. 3, pp. 257–267, march 2001.
[16] D. Weinland, R. Ronfard, and E. Boyer, “Free viewpoint action recognition using motion history volumes,” Computer Vision and Image Understanding, vol. 104, no. 2-3, pp. 249–257, 2006.
[17] M. Blank, L. Gorelick, E. Shechtman, M. Irani, and R. Basri, “Actions as space-time shapes,” IEEE Conference on Computer Vision and Pattern Recognition, 2005.
[18] A. Yilmaz and M. Shah, “Actions sketch: a novel action representation,” IEEE Conference on Computer Vision and Pattern Recognition, 2005.
[19] Z. Jiang, Z. Lin, and L. S. Davis, “Recognizing human actions by learning and matching shape-motion prototype trees,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 34, pp. 533–547, 2012.
[20] D. Tran and A. Sorokin, “Human activity recognition with metric learning,” in Proceedings of European Conference on Computer Vision, 2008.
[21] L. Wang and D. Suter, “Recognizing human activities from silhouettes: Motion subspace and factorial discriminative graphical model,” IEEE Conference on Computer Vision and Pattern Recognition, 2007.
[22] C. Stauffer and W. Grimson, “Adaptive background mixture models for real-time tracking,” IEEE Conference on Computer Vision and Pattern Recognition, 1999
[23] T. Matsuyama, T. Ohya, and H. Habe, “Background subtraction for non-stationary scenes,” Asian Conference of Computer Vision, 2000.
[24] M. Heikkila and M. Pietikainen, “A texture-based method for modeling the background and detecting moving objects,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 28, no. 4, pp. 657–662, 2006.
[25] I. Laptev and T. Lindeberg, “Space-time interest points,” IEEE International Conference on Computer Vision, 2003.
[26] I. Laptev, M. Marszalek, C. Schmid, and B. Rozenfeld, “Learning realistic human actions from movies,” IEEE Conference on Computer Vision and Pattern Recognition, 2008.
[27] P. Scovanner, S. Ali, and M. Shah, “A 3-dimensional sift descriptor and its application to action recognition,” ACM Multimedia, 2007.
[28] P. Dollar, V. Rabaud, G. Cottrell, and S. Belongie, “Behavior recognition via sparse spatio-temporal features,” The 2nd Joint IEEE International Workshop on VS-PETS, 2005.
[29] G. Willems, T. Tuytelaars, and L. Gool, “An efficient dense and scale-invariant spatio-temporal interest point detector,” European Conference on Computer Vision, 2008.
[30] B. Chakraborty, M. B. Holte, T. B. Moeslund, J. Gonzalez, and F. X. Roca, “A selective spatio-temporal interest point detector for human action recognition in complex scenes,” IEEE International Conference on Computer Vision, 2011.
[31] C. Schuldt, I. Laptev, and B. Caputo, “Recognizing human actions: A local svm approach,” International Conference on Pattern Recognition, 2004.
[32] Y. Zhu, X. Zhao, Y. Fu, and Y. Liu, “Sparse coding on local spatial-temporal volumes for human action recognition,” Asian conference on Computer vision, 2010.
[33] D. Weinland and E. Boyer, “Action recognition using exemplar-based embedding,” IEEE Conference on Computer Vision and Pattern Recognition, 2008.
[34] K. Jia and D.-Y. Yeung, “Human action recognition using local spatiotemporal discriminant embedding,” IEEE Conference on Computer Vision and Pattern Recognition, 2008.
[35] P. Natarajan and R. Nevatia, “View and scale invariant action recognition using multiview shape-flow models,” IEEE Conference on Computer Vision and Pattern Recognition, 2008.
[36] D. Weinland, E. Boyer, and R. Ronfard, “Action recognition from arbitrary views using 3d exemplars,” IEEE International Conference on Computer Vision, 2007.
[37] Y. Shen and H. Foroosh, “View-invariant action recognition using fundamental ratios,” IEEE Conference on Computer Vision and Pattern Recognition, 2008.
[38] V. Parameswaran and R. Chellappa, “View invariance for human action recognition,” International Journal of Computer Vision, vol. 66, no. 1, pp. 83–101, 2006.
[39] F. Lv and R. Nevatia, “Single view human action recognition using key pose matching and viterbi path searching,” IEEE Conference on Computer Vision and Pattern Recognition, 2007.
[40] A. Farhadi and M. K. Tabrizi, “Learning to recognize activities from the wrong view point,” European Conference on Computer Vision, 2008.
[41] K. Reddy, J. Liu, , and M. Shah, “Incremental action recognition using feature-tree,” IEEE Conference on Computer Vision, 2009.
[42] P. Yan, S. M. Khan, and M. Shah, “Learning 4d action feature models for arbitrary view action recognition,” IEEE Conference on Computer Vision and Pattern Recognition, 2008.
[43] J. Liu, M. Shah, B. Kuipers, and S. Savarese, “Cross-view action recognition via view knowledge transfer,” IEEE Conference on Computer Vision and Pattern Recognition, 2011.
[44] R. Li and T. Zickler, “Discriminative virtual views for cross-view action recognition,” IEEE Conference on Computer Vision and Pattern Recognition, 2012.
[45] I. N. Junejo, E. Dexter, I. Laptev, and P. Perez, “View-independent action recognition from temporal self-similarities,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 33, no. 1, pp. 172–185, 2011.
[46] M. Holte, B. Chakraborty, J. Gonzalez, and T. Moeslund, “A local 3d motion descriptor for multi-view human action recognition from 4d spatio-temporal interest points,” IEEE Journal of Selected Topics in Signal Processing, vol. 6, no. 5, pp. 553–565, 2012.
[47] A. Iosifidis, A. Tefas, and I. Pitas, “View-invariant action recognition based on artificial neural networks,” IEEE Transactions on Neural Networks and Learning Systems, vol. 23, no. 3, pp. 412–424, Mar 2012.
[48] A. Iosifidis, A. Tefas, and I. Pitas, “Multi-view action recognition based on action volumes, fuzzy distances and cluster discriminant analysis,” Signal Processing, vol. 93, no. 6, pp. 1445–1457, 2013.
[49] A. Iosifidis, A. Tefas, and I. Pitas, “Minimum class variance extreme learning machine for human action recognition,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 23, no. 11, pp. 1968–1979, Nov 2013.
[50] B. Fasel and J. Luettin, “Automatic facial expression analysis: a survey,” Pattern Recognition, vol. 36, no. 1, pp. 259–275, 2003.
[51] C. Sumathi, T. Santhanam, and M.Mahadevi, “Automatic facial expression analysis a survey,” International Journal of Computer Science and Engineering Survey, vol. 3, no. 6, pp. 47–59, 2012.
[52] A. Sanchez, J. Ruiz, M. Ana, Belen, A. Montemayor, H. Javier, and P. J. Jose., “Differential optical flow applied to automatic facial expression recognition,” Neurocomputing, vol. 74, no. 8, pp. 1272–1282, 2011.
[53] M. Bartlett, G. Littlewort, M. Frank, C. Lainscsek, I. Fasel, and J. Movellan, “Recognizing facial expression: machine learning and application to spontaneous behavior,” IEEE Conference on Computer Vision and Pattern Recognition, 2005.
[54] C. Shan, S. Gong, and P. W. McOwan, “Facial expression recognition based on local binary patterns: A comprehensive study,” Image and Vision Computing, vol. 27, pp. 803–816, 2009.
[55] L. Zhong, Q. Liu, P. Yang, B. Liu, J. Huang, and D. Metaxas, “Learning active facial patches for expression analysis,” IEEE Conference on Computer Vision and Pattern Recognition, 2012,
[56] R. Khan, A. Meyer, H. Konik, and S. Bouakaz, “Human vision inspired framework for facial expressions recognition,” IEEE International Conference on Image Processing, 2012.
[57] J. Whitehill and C. Omlin, “Haar features for facs au recognition,” International Conference on Automatic Face and Gesture Recognition, 2006.
[58] L. Fei-Fei and P. Perona, “A bayesian hierarchical model for learning natural scene categories,” IEEE Conference on Computer Vision and Pattern Recognition, 2005.
[59] J. Bassili, “Emotion recognition: The role of facial movement and the relative importance of upper and lower areas of face,” Journal of Personality and Social Psychology, vol. 37, pp. 2049–2059, 1979.
[60] N. S. M. Suma and K. Fujimora, “A preliminary note on pattern recognition of human emotional expression,” Tthe 4th International Joint Conference on Pattern Recognition, 1978.
[61] K. Mase, “Recognition of facial expression from optical flow,” IEICE Transactions on Information and Systems, vol. E74-D, no. 10, pp. 3474–3483, 1991.
[62] Z. Zeng, M. Pantic, G. I. Roisman, and T. Huang, “A survey of affect recognition methods: Audio, visual, and spontaneous expressions,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 31, no. 1, pp. 39–58, Jan 2009.
[63] P. Ekman and W. V. Friesen, “Constants across cultures in the face and emotion,” Journal of Personality and Social Psychology, vol. 17, no. 2, pp. 124–129, 1971
[64] A. M. Elgammal, D. Harwood, and L. S. Davis, “Non-parametric model for background subtraction,” European Conference on Computer Vision, 2000.
[65] Y.-T. Chen, C.-S. Chen, C.-R. Huang, and Y.-P. Hung, “Efficient hierarchical method for background subtraction,” Pattern Recognition, vol. 40, no. 10, pp. 2706–2715, 2007.
[66] N. Martel-Brisson and A. Zaccarin, “Learning and removing cast shadows through a multidistribution approach,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 29, no. 7, pp. 1133 –1146, 2007.
[67] R. Cucchiara, C. Grana, M. Piccardi, and A. Prati, “Detecting moving objects, ghosts, and shadows in video streams,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 25, no. 10, pp. 1337–1342, 2003.
[68] H.-C. Zeng and S.-H. Lai, “Adaptive foreground object extraction for real-time video surveillance with lighting variations,” International Conference on Acoustics, Speech and Signal Processing, 2007.
[69] E. Salvador, A. Cavallaro, and T. Ebrahimi, “Cast shadow segmentation using invariant color features,” Computer Visual Image Understand, vol. 95, no. 2, pp. 238–259, 2004.
[70] W. Zhang, X. Z. Fang, X. Yang, and Q. Wu, “Moving cast shadows detection using ratio edge,” IEEE Transactions on Multimedia, vol. 9, no. 6, pp. 1202–1214, 2007.
[71] R. Souvenir and J. Babbs, “Learning the viewpoint manifold for action recognition,” IEEE Conference on Computer Vision and Pattern Recognition, 2008.
[72] I. N. Junejo, E. Dexter, I. Laptev, and P. Perez, “Cross-view action recognition from temporal self-similarities,” Proceedings of European Conference on Computer Vision, 2008.
[73] D. Weinland, M. Ozuysal, and P. Fua, “making action recognition robust to occlusions and viewpoint changes,” European Conference on Computer Vision, 2010.
[74] M. B. Holte, T. B. Moeslund, C. Tran, and M. M. Trivedi, “Human action recognition using multiple views: a comparative perspective on recent developments,” The joint ACM workshop on Human gesture and behavior understanding, 2011.
[75] D. Dai, W. Yang, and T. Wu, “Three-layer spatial sparse coding for image classification,” International Conference on Pattern Recognition, 2010.
[76] S.-H. Gao, L.-T. Chia, and I.-H. Tsang, “Multi-layer group sparse coding for concurrent image classification and annotation,” IEEE Conference on Computer Vision and Pattern Recognition, 2011.
[77] Z. Wang, S. Wang, and Q. Ji, “Capturing complex spatio-temporal relations among facial muscles for facial expression recognition,” IEEE Conference on Computer Vision and Pattern Recognition, 2013.
[78] B. Jiang, M. Valstar, and M. Pantic, “Action unit detection using sparse appearance descriptors in space-time video volumes,” IEEE International Conference on Automatic Face Gesture Recognition, 2011.
[79] S. Yang, L. An, B. Bhanu, and N. Thakoor, “Improving action units recognition using dense flow-based face registration in video,” IEEE International Conference and Workshops on Automatic Face and Gesture Recognition, 2013.
[80] T. Wu, N. J. Butko, P. Ruvolo, J. Whitehill, M. Bartlett, and J. R. Movellan, “Multilayer architectures for facial action unit recognition,” IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, vol. 42, no. 4, pp. 1027–1038, Aug 2012.
[81] S. Zafeiriou and M. Petrou, “Sparse representations for facial expressions recognition via l1 optimization,” IEEE Conference on Computer Vision and Pattern Recognition Workshops , 2010.
[82] W. Liu, C. Song, and Y. Wang, “Facial expression recognition based on discriminative dictionary learning,” IEEE International Conference on Pattern Recognition, 2012.
[83] J. Wang, J. Yang, K. Yu, F. Lv, T. Huang, and Y. Gong, “Locality-constrained linear coding for image classification,” IEEE Conference on Computer Vision and Pattern Recognition, 2010.
[84] S. H. Lee, H. Kim, Y. M. Ro, and K. N. Plataniotis, “Using color texture sparsity for facial expression recognition,” IEEE International Conference on Automatic Face and Gesture Recognition, 2013.
[85] P. Felzenszwalb and D. Huttenlocher, ”Efficient belief propagation for early vision,” International Journal of Computer Vision, Vol. 70, pp.41–54, 2006
[86] B. J. Frey and D. Dueck, “Clustering by passing messages between data points,” Science, vol. 315, no. 5814, pp. 972–976, 2007.
[87] Z. Zivkovic and F. van der Heijden, “Recursive unsupervised learning of finite mixture models,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 26, no. 5, pp. 651–656, May 2004.
[88] J. Friedman, T. Hastie, and R. Tibshirani, “A note on the group lasso and a sparse group lasso,” pp. 1–8, 2010.
[89] M. Grant and S. Boyd, “Cvx:matlab software for disciplined convex programming, version 1.21, http://cvxr.com/cvx,” April 2011.
[90] L. Grady, “Random walks for image segmentation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 28, no. 1, pp. 83–101, Nov 2006.
[91] J. Shi and C. Tomasi, “Good features to track,” IEEE Conference on Computer Vision and Pattern Recognition, 1994.
[92] B. D. Lucas and T. Kanade, “An iterative image registration technique with an application to stereo vision,” International Joint Conference on Artificial Intelligence, 1981.
[93] P. Lucey, J. Cohn, T. Kanade, J. Saragih, Z. Ambadar, and I. Matthews, “The extended cohn-kanade dataset (CK+): A complete dataset for action unit and emotion-specified expression,” IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2010.
[94] Y. Sun, “Iterative relief for feature weighting,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 29, no. 6, pp. 1–17, Jun 2007.
[95] W. Zheng, “Multi-view facial expression recognition based on group sparse reduced-rank regression,” IEEE Transactions on Affective Computing, vol. 5, no. 1, pp. 71–85, Jan 2014.
[96] M. Liu, S. Li, S. Shan, and X. Chen, “Au-aware deep networks for facial expression recognition,” IEEE International Conference on Automatic Face and Gesture Recognition, 2013.
[97] P. Liu, S. Han, Z. Meng, and Y. Tong, “Facial expression recognition via a boosted deep belief network,” IEEE Conference on Computer Vision and Pattern Recognition, June 2014.
[98] N. Kumar, A. C. Berg, P. N. Belhumeur, and S. K. Nayar, “Attribute and simile classifiers for face verification,” IEEE International Conference on Computer Vision, 2009.
[99] N. Gkalelis, H. Kim, A. Hilton, N. Nikolaidis, and I. Pitas, “The i3dpost multi-view and 3d human action/interaction,” Conference for Visual Media Production, 2009.
[100] J. Liu, S. Ali, and M. Shah, “Recognizing human actions using multiple features,” IEEE Conference on Computer Vision and Pattern Recognition, 2008.
[101] G. J. Burghouts, “Soft-assignment random-forest with an application to discriminative representation of human actions in videos,” International Journal of Pattern Recognition and Artificial Intelligence, vol. 27, pp. 1–16, 2013.
[102] H. Wang, A. Klaser, C. Schmid, and C.-L. Liu, “Dense trajectories and motion boundary descriptors for action recognition,” International Journal of Computer Vision, vol. 103, pp. 66–79, 2013.
[103] M. Holte, T. Moeslund, N. Nikolaidis, and I. Pitas, “3d human action recognition for multi-view camera systems,” International Conference on 3D Imaging, Modeling, Processing, Visualization and Transmission, 2011.
[104] A. Iosifidis, N. Nikolaidis, and I. Pitas, “Movement recognition exploiting multi-view information,” International Workshop Multimedia Signal Process, 2009.
[105] A. Iosifidis, A. Tefas, N. Nikolaidis, and I. Pitas, “Multi-view human movement recognition based on fuzzy distances and linear discriminant analysis,” Computer Vision and Image Understanding, vol. 116, no. 3, pp. 347–360, 2012.
[106] A. Iosifidis, A. Tefas, and I. Pitas, “Multi-view human action recognition under occlusion based on fuzzy distances and neural networks,” Proceedings of the European Signal Processing Conference, 2012.
[107] M. F. Valstar and M. Pantic, “Induced disgust, happiness and surprise: an addition to the mmi facial expression database,” Proceedings of International Conference Language Resources and Evaluation, Workshop on EMOTION, May 2010.
[108 ] N. Aifanti, C. Papachristou, and A. Delopoulos, “The mug facial expression database,” International Workshop on Image Analysis for Multimedia Interactive Services, 2010.
[109] Y. Li, S. Wang, Y. Zhao, and Q. Ji, “Simultaneous facial feature tracking and facial expression recognition,” IEEE Transactions on Image Processing, vol. 22, no. 7, pp. 2559–2573, Jul 2013.
[110] Y.-H. Tu and C.-T. Hsu, “Dual subspace nonnegative matrix factorization for person-invariant facial expression recognition,” IEEE International Conference on Pattern Recognition, 2012.
[111] L. Zhang and D. Tjondronegoro, “Facial expression recognition using facial movement features,” IEEE Transactions on Affective Computing, vol. 2, no. 4, pp. 219–229, Oct 2011.
[112 ] S. L. Happy and A. Routray, “Automatic facial expression recognition using features of salient facial patches,” IEEE Transactions on Affective Computing, vol. 6, no. 1, pp. 1–12, Jan 2015.
[113 ] M. A. Jaffar and E. A. Eisa, “Classification of facial expression using transformed features,” International Journal of Information and Electronics Engineering, vol. 4, no. 4, pp. 269–273, Jul 2014.
[114] J. Yang, J. Wright, T.S. Huang, and Y. Ma, “Image Super-Resolution Via Sparse Representation, ” IEEE Transactions on Image Processing, vol.19, no.11, pp.2861–2873, Nov 2010