International Journal of Image, Graphics and Signal Processing (IJIGSP)
IJIGSP Vol. 10, No. 11, 8 Nov. 2018
Cover page and Table of Contents: PDF (size: 620KB)
Facial Expression Recognition by Holistic and Geometrically Integrated Subspace
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Author(s)
G. P. Hegde
1,*
M. Seetha
2
Nagaratna P Hegde
3
Index Terms
Discriminant analysis, Gabor filter, Expression recognition, Feature extraction, Subspace, geometrical feature
Abstract
This paper demonstrates mainly on feature extraction by analytic and holistic methods and proposes a novel approach for feature level fusion for efficient expression recognition. Gabor filter magnitude feature vector is fused with upper part geometrical features and phase feature vector is fused with lower part geometrical features respectively. Both these high dimensional feature dataset has been projected into low dimensional subspace for de-correlating the feature data redundancy by preserving local and global discriminative features of various expression classes of JAFFE, YALE and FD databases. The effectiveness of subspace of fused dataset has been measured with different dimensional parameters of Gabor filter. The experimental results reveal that performance of the subspace approaches for high dimensional proposed feature level fused dataset compared with state of art approaches.
Cite This Paper
G.P.Hegde, Seetha M., Nagartna P Hegde, "Facial Expression Recognition by Holistic and Geometrically Integrated Subspace", International Journal of Image, Graphics and Signal Processing(IJIGSP), Vol.10, No.11, pp. 54-64, 2018. DOI: 10.5815/ijigsp.2018.11.06
Reference
[1]Rizwan Ahmed Khan, (2013). Ph D thesis on, “Detection of emotions from video in non-controlled environment,” (Public defense).
[2]P. Ekman, W. Friesen, (2003). “Facial Action Coding System: A Technique for the Measurement of Facial Movements,” Consulting Psychologists Press, California, 19.
[3]Peng, H., Long, F., and Ding, C. (2005). “Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 27, No. 8, pp.1226-1238.
[4]Gang Bai. (2009). “Facial Expression Recognition Based on Fusion Features of LBP and Gabor with LDA,” 2nd International Congress on Image and Signal Processing, E-ISBN : 978-1-4244-4131-0,, pp 1-5.
[5]H. Yu and J. Yang, (2001). “A direct LDA algorithm for high dimensional data - with application to face recognition,” Pattern Recognition, vol. 34, 2067-2070.
[6]Shufu Xie, Shiguang Shan, Xilin Chen, Jie Chen, (2010). “Fusing Local Patterns of Gabor Magnitude and Phase for Face Recognition,” IEEE Transactions on Image Processing Volume:19 , Issue: 5. pp1349-1361.
[7]P. Belhumeur, J. Hespanda, and D. Kiregeman, (1997) “Eigenfaces versus Fisherfaces: Recognition using class specific linear projection,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 19, no. 7, pp. 711–720.
[8]Z. Li, D. Lin, and X. Tang, (2009). “Nonparametric discriminant analysis for face recognition,” IEEE Transactions. Pattern Analysis. Machine. Intelligence., vol. 31, no. 4, pp. 755–761.
[9]Yue Ming, Qiuqi Ruan, Xiaoli Li, Meiru Mu, (2010). “Efficient Kernel Discriminate Spectral Regression for 3D Face Recognition,” Proceedings Of ICSP, pp:662 - 665 .
[10]Rahulamathavan, Yogachandran, RC-W. Phan, Jonathon A. Chambers, and David J. Parish, (2013). "Facial expression recognition in the encrypted domain based on local fisher discriminant analysis," Affective Computing, IEEE Transactions on 4, no. 1, 83-92.
[11]S L Happy, Aurobinda Routray, (2015). “Automatic Facial Expression Recognition Using Features of Salient Facial Patches,” IEEE Transaction on Affective Computing, Vol.6, No. 1.
[12]Ashok Rao, S. Noushath, (2010). “Subspace methods for face recognition –survey,” Elsevier, Computer Science Review by Science Direct pp 1-17 –ELSIVER Series.
[13]St.George, B., Chang, C. C., Lin, C.J., (2013). “Facial expression recognition with SMS alert,” IEEE International Conference on Optical Imaging Sensor and Security (ICOSS).
[14]Vinay Bettadapura, “Face Expression Recognition and Analysis: The State of the Art” URL: https://arxiv.org/ftp/arxiv/papers/1203/1203.6722.pdf.
[15]GRS Murthy, R.S. Jadon, (2000). “Effectiveness for Eigenspaces for Facial Expressions Recognition,” International Journal of Computer Theory and Engineering, Vol. 1, No. 3. 1793-8201.
[16]Jamal Hussain Shah, Muhammad Sharif, Mudassar Raza, and Aisha Azeem, (2013). “A Survey: Linear and Nonlinear PCA Based Face Recognition Techniques,” The International Arab Journal of Information Technology, Vol. 10, No. 6.
[17]Dimitris Bolis, Anastasios Maronidis, Anastasios Tefas, and Ioannis Pitas, (2010) “Improving the Robustness of Subspace Learning Techniques for Facial Expression Recognition,” pp. 470–479.
[18]A. Adeel Mohammed, M. A. Sid-Ahmed Rashid Minhas, and Q. M. Jonathan Wu, (2011) “A face portion based recognition system using multidimensional PCA,” IEEE 54th International Midwest Symposium on Circuits and Systems, pp. 1 – 4.
[19]Abegaz, T., Adams J, Bryant, K., Dozier, G., Popplewell, K., Ricanek, K., Shelton, J., and Woodard, D.L., (2011). “Hybrid GAs for Eigen-based facial recognition,” IEEE Workshop on Computational Intelligence in Biometrics and Identity Management, pp. 127 – 130.
[20]Chengliang Wang, LibinLan, MinjieGu, and Yuwei Zhang, (2011). “Face Recognition Based on Principle Component Analysis and Support Vector Machine,” 3rd International Workshop on Intelligent Systems and Applications, pp. 1 – 4.
[21]Vitomir Struc and Nikola Pavesic, (2010). “The Complete Gabor–Fisher Classifier for Robust Face Recognition,” EURASIP Journals on Advances in Signal Processing.
[22]Pedro Alexandre Dias Martins, (2008). “Active Appearance Models for Facial Expression Recognition and Monocular Head Pose Estimation,” Master of Science thesis.
[23]G.P. Hegde, , M. Seetha, Nagaratna Hegde, (2016). “Kernel Locality Preserving Symmetrical Weighted Fisher Discriminant Analysis based subspace approach for expression recognition”, Engineering Science and Technology, an International Journal. Elsevier Publishers.
[24]G. P. Hegde , Shubhada V. P. , M. Seetha , Nagaratna P. Hegde, (2016). “Recognition of Expressions Based on Kernel Global and Local Symmetrical Weighted Fisher Discriminant Nonlinear Subspace Approach,” International Journal of Applied Engineering Research ISSN 0973-4562 Volume 11, Number 5. pp 3558-3569.
[25]Anil Jain, Karthik Nandakumar, Arun Ross, (2005). “Score normalization in multimodal biometric systems,” International Journal of Pattern Recognition 38, 2270– 2285. ELSEVIER Publishers.
[26]C.W.Hsu,C.C. Chang and G.J.Lin. (2009). A practical guide to support vector classification. :http//www.csie.ntu.edu.tw/cjlin/papers/guid e/guide.pdf.
[27]http://www.kasrl.org/jaffe.html
[28]Shiqing Zhang, Xiaoming Zhao, Bicheng Lei, (2012). “Facial Expression Recognition Based on Local Binary Patterns and Local Fisher Discriminant Analysis,” Wseas Transaction on Signal Processing Issue 1, Volume 8.
[29]I. Cohen, N. Sebe, A. Garg, L. Chen, T.S. Huang, (2003). “Facial expression recognition from video sequences: temporal and static modeling,” Computer Vision and Image Understanding 91, 160–187.
[30]R Zhi, Q Ruan, (2008). Facial expression recognition based on two dimensional discriminant locality preserving projections. Neuro Computing. 71, 1730–1734.
[31]Shi Dongcheng , Cai Fang, (2013). “Facial Expression Recognition Based on Gabor Wavelet Phase Features,” pp 520 – 523.
[32]W Liejun, Q Xizhong, Z Taiyi, (2009). “Facial expression recognition using improved support vector machine by modifying kernels,” Information. Technology. 8(4), pp 595–599.
[33]Chien-Cheng Lee, Shin-Sheng Huang and Cheng-Yuan Shih, (2010). “Facial Affect Recognition Using Regularized Discriminant Analysis-Based Algorithms,” EURASIP Journal on Advances in Signal Processing.
[34]L Zhao, G Zhuang, X Xu, (2008). “Facial expression recognition based on PCA and NMF,” Proceeding of the 7th World Congress on Intelligent Control and Automation, pp. 6822–6825.
[35]Wang Z., Ruan Q., (2010). “Facial Expression Recognition Based Orthogonal Local Fisher Discriminant Analysis,” Proceedings of the International Conference on Signal Processing (ICSP), pp. 1358–1361.
[36]F. Y. Shih, C.-F. Chuang, and P. S. P. Wang, (2008). “Performance comparisons of facial expression recognition in JAFFE database,” International Journal of Pattern Recognition and Artificial Intelligence, vol. 22, no. 3, pp. 445–459.
[37]Z Zhang, M Lyons, M Schuster, S Akamatsu, (1998) “Comparison between geometry based and Gabor wavelet based facial expression recognition using multi layer perceptron ,” Proceeding 3rd International Conference on Automatic Face and Gesture Recognition, pp. 454–459.
[38]H. Ling, S. Soatto, N. Ramanathan, and D. Jacobs, (2010). “Face verification across age progression using discriminative methods,” IEEE Transactions. On Information. Forensic Security, vol. 5, no. 1, pp. 82–91.
[39]X. Wang and X. Tang, (2004). “Random sampling LDA for face recognition,” in Proceedings of IEEE Conference of Computer Vision & Pattern Recognition., pp.259–265.
[40]Jiadong Song, Xiaojuan Li, Pengfei Xu, Mingquan Zhou, (2011). “A New Face Recognition Framework: Symmetrical Bilateral 2DPLS plus LDA,” Journal of Multimedia, Vol. 6, No. 1.
[41]M. Loog, R. P. W. Duin and R. Hacb-Umbach, (2001). “Multi-class Linear Dimension Reduction by Weighted Pair Wise Fisher Criteria,” IEEE Transaction on Pattern Recognition and Machine Intelligence, Vol. 23, No. 7, pp. 762-766.
[42]E. K. Tang, P. N. Suganthan, X. Yao and A. K. Qin, (2003). “Linear Dimensionality Reduction Using Relevance Weighted LDA,” Pattern Recognition, Vol. 38, No. 4, pp. 485-493.
[43]X. He and P. Niyogi, (2003). “Locality preserving projections,” In Advances in Neural Information Processing Systems.
[44]Lin Kezheng, Lin Sheng and Chen Dongmei, (2008). “Enhanced Locality Preserving Projections,”. CSSE (1), 985-988.
[45]Xiaofei He, Shuicheng Yan, Yuxiao Hu, Partha Niyogi, and Hong- Jiang Zhang, (2005). “Face RecognitionUsing Laplacianfaces”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, No. 3.
[46]Yi Jin and Qiu-Qi Ruan, (2007). “An image matrix compression based supervised LPP for face recognition,” International Symposium on Intelligent Signal Processing and Communication Systems.
[47]Abdulrahman, Muzammil, Tajuddeen R. Gwadabe, Fahad J. Abdu, and Alaa Eleyan. (2014) “Gabor wavelet transform based facial expression recognition using PCA and LBP," In Signal Processing and Communications Applications IEEE Conference and Communications Applications pp. 2265-2268.
[48]Shuaishi Liu, Ning Ding, Mao Yang, Keping Liu, (2011). “Facial Expression Recognition Method Based on the Fusion of Geometry Features and Texture Features,” International Conference on Electrical and Computer Engineering, Advances in Biomedical Engineering, Vol.11.
[49]Ziqiang Wang, Xia Sun, (2012). “Manifold Adaptive Kernel Local Discriminant Analysis for face Recognition,” Journal of Multimedia Vol 7. No. 9.
[50]M. Sugiyama, (2006). “Dimensionality reduction of multimodal labeled data by local Fisher discriminant analysis,” Journal of Machine Learning Research, vol.8, pp.1027- 1061.
[51]Hong-Bo Deng, Lian-Wen Jin, Li-Xin Zhen, Ian-Cheng Huang, (2005). “A New Facial Expression Recognition Method Based on Local Gabor Filter Bank and PCA plus LDA,” International Journal of Information Technology Vol.11, No.11.
[52]Wang Zhen, Ying Zilu; (2012). “Facial expression recognition based on adaptive local binary pattern and sparse representation”, IEEE.
[53]Zhaoyu Wang , Shangfei Wang, (2011). “Spontaneous facial expression recognition by using feature-level fusion of visible and thermal infrared images,” 2011 IEEE International Workshop on Machine Learning for Signal Processing - 2011 pp:1 – 6.
[54]Thiago H.H. Zavaschi, Alceu S. Britto Jr., Luiz E.S. Oliveira, (2013) Alessandro L. Koerich Fusion of feature sets and classifiers for facial expression recognition Expert Systems with Applications, 40, pp. 646–655, 20
[55]Rongrong Fu, Hong Wang ,, Wenbo Zhao, (2016). “Dynamic driver fatigue detection using hidden Markov model in real driving condition,” Expert Systems with Applications, Volume 63, pp. 397–411.