Volume 15, Issue 2 (9-2018)                   JSDP 2018, 15(2): 69-88 | Back to browse issues page


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Mohammadi Dashti M, Harouni M. Smile and Laugh Expressions Detection Based on Local Minimum Key Points. JSDP. 2018; 15 (2) :69-88
URL: http://jsdp.rcisp.ac.ir/article-1-658-en.html
Faculty of Computer Engineering, Dolatabad Branch, Islamic Azad University
Abstract:   (203 Views)

In this paper, a smile and laugh facial expression is presented based on dimension reduction and description process of the key points. The paper has two main objectives; the first is to extract the local critical points in terms of their apparent features, and the second is to reduce the system’s dependence on training inputs. To achieve these objectives, three different scenarios on extracting the features are proposed. First of all, the discrete parts of a face are detected by local binary pattern method that is used to extract a set of global feature vectors for texture classification considering various regions of an input-image face. Then, in the first scenario and with respect to the correlation changes of adjacent pixels on the texture of a mouth area, a set of local key points are extracted using the Harris corner detector. In the second scenario, the dimension reduction of the extracted points of first scenario provided by principal component analysis algorithm leading to reduction in computational costs and overall complexity without loss of performance and flexibility; and in the final scenario, a set of critical points is extracted through comparing the extracted points’ coordinates of the first scenario and the BRISK Descriptor, which is utilized a neighborhood sampling strategy of directions for a key-point. In the following, without training the system, facial expressions are detected by comparing the shape and the geometric distance of the extracted local points of the mouth area. The well-known standard Cohn-Kaonde, CAFÉ, JAFFE and Yale benchmark dataset are applied to evaluate the proposed approach. The results shows an overall enhancement of 6.33% and 16.46% for second scenario compared with first scenario and third scenario compared with second scenario. The experimental results indicate the power efficiency of the proposed approach in recognizing images more than 90 % across all the datasets.
 
 

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Type of Study: Research | Subject: Paper
Received: 2017/03/31 | Accepted: 2018/05/16 | Published: 2018/09/17 | ePublished: 2018/09/17

References
1. [1] J. L. Lakin, "Automatic cognitive processes and nonverbal communication," The Sage handbook of nonverbal communication, pp. 59-77, 2006. [PMCID]
2. [2] M. Harouni, D. Mohamad, and A. Rasouli, "Deductive method for recognition of on-line handwritten Persian/Arabic characters," in Computer and Automation Engineering (ICCAE), 2010 The 2nd International Conference on, 2010, vol. 5, pp. 791-795: IEEE.
3. [3] L. Sánchez López, "Local Binary Patterns applied to Face Detection and Recognition," 2010.
4. [4] N. M. C. Shaghayegh Naderi, Esanollah Kabir, "Region-based Quality Improvement of Facial Images with Strong Shadows to Enhance Recognition," (in eng), Signal and Data Processing, Research vol. 8, no. 1, pp. 55-66, 2011.
5. [5] M. Harouni, M. Rahim, M. Al-Rodhaan, T. Saba, A. Rehman, and A. Al-Dhelaan, "Online Persian/Arabic script classification without contextual information," The Imaging Science Journal, vol. 62, no. 8, pp. 437-448, 2014. [DOI:10.1179/1743131X14Y.0000000083]
6. [6] D. A. Lisin, M. A. Mattar, M. B. Blaschko, E. G. Learned-Miller, and M. C. Benfield, "Combining local and global image features for object class recognition," in Computer vision and pattern recognition-workshops, 2005. CVPR workshops. IEEE Computer society conference on, 2005, pp. 47-47: IEEE.
7. [7] S. Z. Seyyedsalehi and S. A. Seyyedsalehi, "Improving the nonlinear manifold separator model to the face recognition by a single image of per person," (in eng), Signal and Data Processing, Research vol. 12, no. 1, pp. 3-16, 2015.
8. [8] M. Harouni, D. Mohamad, M. S. M. Rahim, S. M. Halawani, and M. Afzali, "Handwritten Arabic character recognition based on minimal geometric features," International Journal of Machine Learning and Computing, vol. 2, no. 5, p. 578, 2012. [DOI:10.7763/IJMLC.2012.V2.193]
9. [9] A. J. Calder, A. M. Burton, P. Miller, A. W. Young, and S. Akamatsu, "A principal component analysis of facial expressions," Vision research, vol. 41, no. 9, pp. 1179-1208, 2001. [DOI:10.1016/S0042-6989(01)00002-5]
10. [10] S. Jairath, S. Bharadwaj, M. Vatsa, and R. Singh, "Adaptive skin color model to improve video face detection," in Machine Intelligence and Signal Processing: Springer, 2016, pp. 131-142. [DOI:10.1007/978-81-322-2625-3_12]
11. [11] D. Reska, C. Boldak, and M. Kretowski, "A texture-based energy for active contour image segmentation," in Image Processing & Communications Challenges 6: Springer, 2015, pp. 187-194.
12. [12] M. Turk and A. Pentland, "Eigenfaces for recognition," Journal of cognitive neuroscience, vol. 3, no. 1, pp. 71-86, 1991. [DOI:10.1162/jocn.1991.3.1.71] [PMID]
13. [13] S. Toyota, I. Fujiwara, M. Hirose, N. Ojima, K. Ogawa-Ochiai, and N. Tsumura, "Principal component analysis for the whole facial image with pigmentation separation and application to the prediction of facial images at various ages," Journal of Imaging Science and Technology, vol. 58, no. 2, pp. 20503-1-20503-11, 2014.
14. [14] M. H. Siddiqi, R. Ali, A. M. Khan, Y.-T. Park, and S. Lee, "Human facial expression recognition using stepwise linear discriminant analysis and hidden conditional random fields," IEEE Transactions on Image Processing, vol. 24, no. 4, pp. 1386-1398, 2015. [DOI:10.1109/TIP.2015.2405346] [PMID]
15. [15] J. Lu, K. N. Plataniotis, and A. N. Venetsanopoulos, "Face recognition using kernel direct discriminant analysis algorithms," IEEE Transactions on Neural Networks, vol. 14, no. 1, pp. 117-126, 2003. [DOI:10.1109/TNN.2002.806629] [PMID]
16. [16] M. S. Bartlett, J. R. Movellan, and T. J. Sejnowski, "Face recognition by independent component analysis," IEEE transactions on neural networks/a publication of the IEEE Neural Networks Council, vol. 13, no. 6, p. 1450, 2002.
17. [17] R. Okamoto, S. Bando, and A. Nozawa, "Blind signal processing of facial thermal images based on independent component analysis," IEEJ Transactions on Electronics, Information and Systems, vol. 136, no. 8, pp. 1142-1148, 2016. [DOI:10.1541/ieejeiss.136.1142]
18. [18] E. Rosten and T. Drummond, "Machine learning for high-speed corner detection," in European conference on computer vision, 2006, pp. 430-443: Springer.
19. [19] H. Yoo, U. Yang, and K. Sohn, "Gradient-enhancing conversion for illumination-robust lane detection," IEEE Transactions on Intelligent Transportation Systems, vol. 14, no. 3, pp. 1083-1094, 2013. [DOI:10.1109/TITS.2013.2252427]
20. [20] B.-W. Chen, S. Rho, M. Guizani, and W.-K. Fan, "Cognitive sensors based on ridge phase-smoothing localization and multiregional histograms of oriented gradients," IEEE Transactions on Emerging Topics in Computing, 2016. [DOI:10.1109/TETC.2016.2585040]
21. [21] B. Bell and L.-F. Pau, "Contour tracking and corner detection in a logic programming environment," IEEE Transactions on pattern analysis and machine intelligence, vol. 12, no. 9, pp. 913-917, 1990. [DOI:10.1109/34.57685]
22. [22] S. Agarwal and D. P. Mukherjee, "Facial expression recognition through adaptive learning of local motion descriptor," Multimedia Tools and Applications, vol. 76, no. 1, pp. 1073-1099, 2017. [DOI:10.1007/s11042-015-3103-6]
23. [23] M. Harouni, D. Mohamad, M. S. M. Rahim, and S. M. Halawani, "Finding Critical Points of Handwritten Persian/Arabic Character," International Journal of Machine Learning and Computing, vol. 2, no. 5, p. 573, 2012. [DOI:10.7763/IJMLC.2012.V2.192]
24. [24] G. Gao, K. Jia, and B. Jiang, "An Automatic Geometric Features Extracting Approach for Facial Expression Recognition Based on Corner Detection," in Intelligent Information Hiding and Multimedia Signal Processing (IIH-MSP), 2015 International Conference on, 2015, pp. 302-305: IEEE.
25. [25] Z. H. Shah and V. Kaushik, "Performance analysis of canny edge detection for illumination invariant facial expression recognition," in Industrial Instrumentation and Control (ICIC), 2015 International Conference on, 2015, pp. 584-589: IEEE.
26. [26] H. Candra, M. Yuwono, R. Chai, H. T. Nguyen, and S. Su, "Classification of facial-emotion expression in the application of psychotherapy using Viola-Jones and Edge-Histogram of Oriented Gradient," in Engineering in Medicine and Biology Society (EMBC), 2016 IEEE 38th Annual International Conference of the, 2016, pp. 423-426: IEEE. [PMID]
27. [27] R. Agada and J. Yan, "Edge based mean LBP for valence facial expression detection," in Electrical, Computer and Communication Technologies (ICECCT), 2015 IEEE International Conference on, 2015, pp. 1-7: IEEE.
28. [28] S. Ahmadkhani and P. Adibi, "Supervised Probabilistic Principal Component Analysis Mixture Model in a Lossless Dimensionality Reduction Framework for Face Recognition," Signal and Data Processing, vol. 12, no. 4, pp. 53-65, 2016.
29. [29] T. Kanade, Y. Tian, and J. F. Cohn, "Comprehensive database for facial expression analysis," in fg, 2000, p. 46: IEEE.
30. [30] M. Lyons, S. Akamatsu, M. Kamachi, and J. Gyoba, "Coding facial expressions with gabor wavelets," in Automatic Face and Gesture Recognition, 1998. Proceedings. Third IEEE International Conference on, 1998, pp. 200-205: IEEE.
31. [31] M. Dailey, G. Cottrell, and J. Reilly, "California facial expressions (cafe)," Unpublished digital images, University of California, San Diego, Computer Science and Engineering Department, 2001.
32. [32] A. Georghiades, P. Belhumeur, and D. Kriegman, "Yale face database," Center for computational Vision and Control at Yale University, http://cvc. yale. edu/projects/yalefaces/yalefa, vol. 2, p. 6, 1997.
33. [33] V. Tipsuwanpom, V. Krongratana, S. Gulpanich, and K. Thongnopakun, "Fire detection using neural network," in SICE-ICASE, 2006. International Joint Conference, 2006, pp. 5474-5477: IEEE.
34. [34] P. Ji, Y. Kim, Y. Yang, and Y.-S. Kim, "Face occlusion detection using skin color ratio and LBP features for intelligent video surveillance systems," in Computer Science and Information Systems (FedCSIS), 2016 Federated Conference on, 2016, pp. 253-259: IEEE. [PMID]
35. [35] O. H. Jensen, "Implementing the Viola-Jones face detection algorithm," Technical University of Denmark, DTU, DK-2800 Kgs. Lyngby, Denmark, 2008.
36. [36] C. Harris and M. Stephens, "A combined corner and edge detector," in Alvey vision conference, 1988, vol. 15, no. 50, pp. 10-5244: Citeseer.
37. [37] X. Shunqing, Z. Weihong, and X. Wei, "Optimization of Harris corner detection algorithm," in Advances in Control and Communication: Springer, 2012, pp. 59-64.
38. [38] M. N. Patil, B. Iyer, and R. Arya, "Performance Evaluation of PCA and ICA Algorithm for Facial Expression Recognition Application," in Proceedings of Fifth International Conference on Soft Computing for Problem Solving, 2016, pp. 965-976: Springer.
39. [39] J. H. Shah, M. Sharif, M. Raza, and A. Azeem, "A Survey: Linear and Nonlinear PCA Based Face Recognition Techniques," Int. Arab J. Inf. Technol., vol. 10, no. 6, pp. 536-545, 2013.
40. [40] S. Leutenegger, M. Chli, and R. Y. Siegwart, "BRISK: Binary robust invariant scalable keypoints," in Computer Vision (ICCV), 2011 IEEE International Conference on, 2011, pp. 2548-2555: IEEE.
41. [41] M. H. Siddiqi et al., "Human facial expression recognition using curvelet feature extraction and normalized mutual information feature selection," Multimedia Tools and Applications, vol. 75, no. 2, pp. 935-959, 2016. [DOI:10.1007/s11042-014-2333-3]
42. [42] Y. Cao, W. Zheng, L. Zhao, and C. Zhou, "Expression recognition using elastic graph matching," in International Conference on Affective Computing and Intelligent Interaction, 2005, pp. 8-15: Springer. [DOI:10.1007/11573548_2]
43. [43] M. J. Lyons, J. Budynek, and S. Akamatsu, "Automatic classification of single facial images," IEEE transactions on pattern analysis and machine intelligence, vol. 21, no. 12, pp. 1357-1362, 1999. [DOI:10.1109/34.817413]
44. [44] W. Gu, C. Xiang, Y. Venkatesh, D. Huang, and H. Lin, "Facial expression recognition using radial encoding of local Gabor features and classifier synthesis," Pattern recognition, vol. 45, no. 1, pp. 80-91, 2012. [DOI:10.1016/j.patcog.2011.05.006]

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