zbMATH — the first resource for mathematics

Emotion recognition using eigenvalues and Levenberg-Marquardt algorithm-based classifier. (English) Zbl 1360.94039
Summary: In this paper, a simple and computationally efficient approach is proposed for person independent facial emotion recognition. The proposed approach is based on the significant features of an image, i.e., the collection of few largest eigenvalues (LE). Further, a Levenberg-Marquardt algorithm-based neural network (LMNN) is applied for multiclass emotions classification. This leads to a new facial emotion recognition approach (LE-LMNN) which is systematically examined on JAFFE and Cohn-Kanade databases. Experimental results illustrate that the LE-LMNN approach is effective and computationally efficient for facial emotion recognition. The robustness of the proposed approach is also tested on low-resolution facial emotion images. The performance of the proposed approach is found to be superior as compared to the various existing methods.
94A08 Image processing (compression, reconstruction, etc.) in information and communication theory
68T10 Pattern recognition, speech recognition
Cohn-Kanade; JAFFE
Full Text: DOI
[1] Mehrabian A 1971 Silent messages. Wadsworth Publishing Company, Inc. Belmont, California
[2] Moore, S; Bowden, R, Local binary patterns for multi-view facial expression recognition, Comput. Vis. Image Underst., 115, 541-558, (2011)
[3] Tian Y, Kanade T and Cohn J 2005 Facial expression analysis. Handbook of face recognition, Springer (chapter 11)
[4] Gaidhane, VH; Hote, YV; Singh, V, An efficient approach for face recognition based on common eigenvalues, Pattern Recognit., 47, 1869-1879, (2014) · Zbl 1290.93006
[5] Fasel, B; Luttin, J, Automatic facial expression analysis: A survey, Pattern Recognit., 36, 259-275, (2003) · Zbl 1007.68947
[6] Sánchez, A; Ruiz, JV; Moreno, AB; Montemayor, AS; Hernández, J; Pantrigo, JJ, Differential optical flow applied to automatic facial expression recognition, Neurocomputing, 74, 1272-1282, (2011)
[7] Yacoob, Y; Davis, LS, Recognizing human facial expression from long image sequences using optical flow, IEEE Trans. Pattern Anal. Mach. Intell., 18, 636-642, (1996)
[8] Pantic, M; Rothkrantz, L, Expert system for automatic analysis of facial expression, Image Vis. Comput., 18, 881-905, (2000)
[9] Tian, YL; Kanade, T; Cohn, J, Recognizing action units for facial expression analysis, IEEE Trans. Pattern Anal. Mach. Intell., 23, 1-19, (2001)
[10] Yang, J; Zhang, D; Frangi, AF; Yang, JY, Two-dimensional PCA: A new approach to appearance-based face representation and recognition, IEEE Trans. Pattern Anal. Mach. Intell., 26, 131-136, (2004)
[11] Eftekhari, A; Forouzanfar, M; Moghaddam, HA; Alirezaie, J, Block-wise 2D kernel PCA/LDA for face recognition, Inf. Process. Lett., 110, 761-767, (2010) · Zbl 1234.68352
[12] Li, M; Yuan, B, 2D-LDA: A statistical linear discriminant analysis for image matrix, Pattern Recognit. Lett., 26, 527-532, (2005)
[13] Ekenel, HK; Sankur, B, Feature selection in the independent component subspace for face recognition, Pattern Recognit. Lett., 25, 377-1388, (2004)
[14] Bashyal, S; Venayagamoorthy, GK, Recognition of facial expressions using Gabor wavelets and learning vector quantization, Eng. Appl. Artif. Intell., 21, 1056-1064, (2008)
[15] Shan, C; Gong, S; Mcowan, PW, Facial expression recognition based on local binary patterns: A comprehensive study, Image Vis. Comput., 27, 803-816, (2009)
[16] Ojala, T; Pietikäinen, M; Harwood, D, A comparative study of texture measures with classification based on feature distribution, Pattern Recognit., 29, 51-59, (1996)
[17] Ojala, T; Pietikäinen, M; Mäenpää, T, Multiresolution gray-scale and rotation invariant texture classification with local binary patterns, IEEE Trans. Pattern Anal. Mach. Intell., 24, 971-987, (2002) · Zbl 0977.68853
[18] Pietikäinen M, Hadid A, Zhao G and Ahonen T 2011 Computer vision using local binary patterns, vol. 40, Springer
[19] Padgett, C; Cottrell, G, Representing face images for emotion classification, Adv. Neural Inf. Proc. Syst., 9, 894-900, (1997)
[20] Tian Y 2004 Evaluation of face resolution for expression analysis. In: Proc. CVPRW’04, pp. 82-82
[21] Lee B, Chun J and Park P 2008 Classification of facial expression using SVM for emotion care service system. In: Proceedings of SNPD’08, pp. 8-12 · Zbl 1116.68109
[22] Cohen, I; Sebe, N; Garg, A; Chen, L; Huang, TS, Facial expression recognition from video sequences: temporal and static modelling, Comput. Vis. Image Underst., 91, 160-187, (2003)
[23] Pantic, M; Rothkrantz, L, Facial action recognition for facial expression analysis from static face images. IEEE trans, Syst. Man Cybern. Part B Cybern., 34, 1449-1461, (2004)
[24] Pantic, M; Rothkrantz, LM, Automatic analysis of facial expressions: the state of the art, IEEE Trans. Pattern Anal. Mach. Intell., 22, 1424-1445, (2000)
[25] Pantic, M; Patras, I, Dynamics of facial expression: recognition of facial actions and their temporal segments from face profile image sequences. IEEE trans, Syst. Man Cybern. Part B Cybern., 36, 433-449, (2006)
[26] Wee, CY; Parmesran, R, Measure of image sharpness using eigenvalues, Inf. Sci., 177, 2533-2552, (2007) · Zbl 1116.68109
[27] Bie T D, Cristianini N and Rosipal R 2005 Eigenproblems in pattern recognition. Handbook of Geometric Computing, Springer Berlin Heidelberg, pp. 129-167
[28] Agarwal, M; Jain, N; Kumar, M; Agrawal, H, Face recognition using eigen faces and artificial neural network, Int. J. Comput. Theor. Eng., 2, 1793-8201, (2010)
[29] Gaidhane, VH; Hote, YV; Singh, V, Nonrigid image registration using efficient similarity measure and Levenberg-Marquardt optimization, Biomed. Eng. Lett., 2, 118-123, (2012)
[30] Gaidhane, VH; Hote, YV; Singh, V; Kumar, M, New approaches for image compression using neural network, J. Intell. Learning Syst. Appl., 3, 220-229, (2011)
[31] Gaidhane, V; Singh, V; Kumar, M, Image compression using PCA and improved technique with MLP neural network, 106-110, (2010), India
[32] Singh, V; Gupta, I; Gupta, HO, ANN-based estimator for distillation using Levenberg-Marquardt approach, Eng. Appl. Artif. Intell., 20, 249-259, (2005)
[33] Hagan M T, Demuth H B and Beale M 2003 Neural network design. 7th International Student Edition. Vikas Publishing House
[34] Lyons, MJ; Budynek, J; Akamatsu, S, Automatic classification of single facial images, IEEE Trans. Pattern Anal. Mach. Intell., 21, 1357-1362, (1999)
[35] Kanade, T; Cohn, JF; Tian, Y, Comprehensive database for facial expression analysis, 46-53, (2000), France
[36] Feng, X; Pietikäinen, M; Hadid, T, Facial expression recognition with local binary patterns and linear programming, Pattern Recognit. Image Anal., 15, 546-548, (2005)
[37] Liao, S; Fan, W; Chung, ACS; Yeung, DY, Facial expression recognition using advanced local binary patterns, Tsallis entropies and global appearance features, 665-668, (2006), GA
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. It attempts to reflect the references listed in the original paper as accurately as possible without claiming the completeness or perfect precision of the matching.