簡易檢索 / 詳目顯示
| 研究生： |
Reza Syahroel Ghufran Reza - Syahroel Ghufran |
|---|---|
| 論文名稱： |
透過混合式優化方案來提高年齡判斷之準確性 Improving the Age Estimation Accuracy by a Hybrid Optimization Scheme |
| 指導教授： |
呂政修
Jenq-Shiou Leu |
| 口試委員: |
陳省隆
Hsing-Lung Chen 阮聖彰 Shanq-Jang Ruan 林昌鴻 Chang Hong Lin |
| 學位類別： |
碩士 Master |
| 系所名稱： |
電資學院 - 電子工程系 Department of Electronic and Computer Engineering |
| 論文出版年： | 2016 |
| 畢業學年度： | 104 |
| 語文別： | 英文 |
| 論文頁數： | 46 |
| 中文關鍵詞： | age estimation 、Principle Component Analysis 、Linear Discriminant Analysis 、Support Vector Machines 、General Algorithm 、Particle Swarm Optimization |
| 外文關鍵詞： | age estimation, Principle Component Analysis, Linear Discriminant Analysis, Support Vector Machines, General Algorithm, Particle Swarm Optimization |
| 相關次數： | 點閱：258 下載：1 |
| 分享至: |
| 查詢本校圖書館目錄 查詢臺灣博碩士論文知識加值系統 勘誤回報 |
Age estimation from digital contents is an interesting topic. Face image reading for age estimation is an intuitive way after classifying face images into several predefined age groups. Age estimation can be regarded as a multiclass problem due to the variation of given individuals determined by genes and several external factors. In the proposed scheme, we conducted an age range estimation with five predefined classes and combined several techniques of extracting estimation information from image data, such as Local Gabor Binary Patterns (LGBP) for filtering, Principle Component Analysis (PCA), Linear Discriminant Analysis (LDA) for feature extraction, Support Vector Machines (SVM) for classification, as well as General Algorithm (GA) and Particle Swarm Optimization (PSO) for optimization. Genetic Algorithm and Particle Swarm Optimization were used to find the most suitable parameters to carry out the SVM method. This work derives the results for both using optimization and without optimization. Experimental results show that our proposed hybrid method can raise the estimation accuracy compared to other schemes. The outcome of fold 3 is enhanced up to 14% for both GA and PSO, and the scheme with GA has diminished processing time up to 26s while it with PSO can reduce up to 25s.
Age estimation from digital contents is an interesting topic. Face image reading for age estimation is an intuitive way after classifying face images into several predefined age groups. Age estimation can be regarded as a multiclass problem due to the variation of given individuals determined by genes and several external factors. In the proposed scheme, we conducted an age range estimation with five predefined classes and combined several techniques of extracting estimation information from image data, such as Local Gabor Binary Patterns (LGBP) for filtering, Principle Component Analysis (PCA), Linear Discriminant Analysis (LDA) for feature extraction, Support Vector Machines (SVM) for classification, as well as General Algorithm (GA) and Particle Swarm Optimization (PSO) for optimization. Genetic Algorithm and Particle Swarm Optimization were used to find the most suitable parameters to carry out the SVM method. This work derives the results for both using optimization and without optimization. Experimental results show that our proposed hybrid method can raise the estimation accuracy compared to other schemes. The outcome of fold 3 is enhanced up to 14% for both GA and PSO, and the scheme with GA has diminished processing time up to 26s while it with PSO can reduce up to 25s.
REFERENCES
1. Sai, P.K., Wang, J.G. and Teoh, E.K., 2015. Facial age range estimation with extreme learning machines. Neurocomputing, 149, pp.364-372.
2. Cote, M. and Albu, A.B., 2015. Robust texture classification by aggregating pixel-based LBP statistics. IEEE Signal Processing Letters, 22(11), pp.2102-2106.
3. Xuefeng, C., Fei, L. and Huang, C., 2014, June. Face recognition by Zero-Ratio based LGBP features. In Intelligent Control and Automation (WCICA), 2014 11th World Congress on (pp. 5605-5608). IEEE.
4. Xie, Z., 2014, July. Infrared face recognition based on lbp co-occurrence matrix. In Control Conference (CCC), 2014 33rd Chinese (pp. 4817-4820). IEEE.
5. Chen, W.N., Zhang, J., Lin, Y., Chen, N., Zhan, Z.H., Chung, H.S.H., Li, Y. and Shi, Y.H., 2013. Particle swarm optimization with an aging leader and challengers. IEEE Transactions on Evolutionary Computation, 17(2), pp.241-258.
6. Choi, S.E., Lee, Y.J., Lee, S.J., Park, K.R. and Kim, J., 2011. Age estimation using a hierarchical classifier based on global and local facial features. Pattern Recognition, 44(6), pp.1262-1281.
7. Fukai, H., Takimoto, H., Fukumi, M. and Mitsukura, Y., 2011. Apparent age estimation system based on age perception. INTECH Open Access Publisher.
8. Hewahi, N., Olwan, A., Tubeel, N., El-Asar, S. and Abu-Sultan, Z., 2010. Age estimation based on neural networks using face features. Journal of Emerging Trends in Computing and Information Sciences, 1(2), pp.61-67.
9. Luu, K., Ricanek, K., Bui, T.D. and Suen, C.Y., 2009, September. Age estimation using active appearance models and support vector machine regression. In Biometrics: Theory, Applications, and Systems, 2009. BTAS'09. IEEE 3rd International Conference on (pp. 1-5). IEEE.
10. Wang, X.M., Huang, C., Ni, G.Y. and Liu, J.G., 2009, October. Face recognition based on face Gabor image and SVM. In Image and Signal Processing, 2009. CISP'09. 2nd International Congress on (pp. 1-4). IEEE.
11. Ye, F., Shi, Z. and Shi, Z., 2009, July. A comparative study of PCA, LDA and Kernel LDA for image classification. In Ubiquitous Virtual Reality, 2009. ISUVR'09. International Symposium on (pp. 51-54). IEEE.
12. Fu, Y. and Huang, T.S., 2008. Human age estimation with regression on discriminative aging manifold. IEEE Transactions on Multimedia, 10(4), pp.578-584.
13. Sun, X., Xu, H., Zhao, C. and Yang, J., 2008, September. Facial expression recognition based on histogram sequence of local Gabor binary patterns. In2008 IEEE Conference on Cybernetics and Intelligent Systems (pp. 158-163). IEEE.
14. Xie, S., Shan, S., Chen, X. and Gao, W., 2008, December. V-LGBP: Volume based local Gabor binary patterns for face representation and recognition. InPattern Recognition, 2008. ICPR 2008. 19th International Conference on (pp. 1-4). IEEE.
15. Takimoto, H., Kuwano, T., Mitsukura, Y., Fukai, H. and Fukumi, M., 2007, September. Appearance-age feature extraction from facial image based on age perception. In SICE, 2007 Annual Conference (pp. 2813-2818). IEEE.
16. Poli, R., Kennedy, J. and Blackwell, T., 2007. Particle swarm optimization.Swarm intelligence, 1(1), pp.33-57.
17. Zhang, W., Shan, S., Chen, X. and Gao, W., 2007. Local Gabor binary patterns based on Kullback–Leibler divergence for partially occluded face recognition. IEEE signal processing letters, 14(11), pp.875-878.
18. Geng, X., Zhou, Z.H., Zhang, Y., Li, G. and Dai, H., 2006, October. Learning from facial aging patterns for automatic age estimation. In Proceedings of the 14th ACM international conference on Multimedia (pp. 307-316). ACM.
19. Rodríguez, M.A. and Jarur, M.C., 2005. A genetic algorithm for searching spatial configurations. IEEE Transactions on Evolutionary Computation, 9(3), pp.252-270.
20. Lanitis, A., Draganova, C. and Christodoulou, C., 2004. Comparing different classifiers for automatic age estimation. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 34(1), pp.621-628.
21. Lanitis, A., Taylor, C.J. and Cootes, T.F., 2002. Toward automatic simulation of aging effects on face images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(4), pp.442-455.
22. Guo, G., Fu, Y., Dyer, C.R. and Huang, T.S., 2008. Image-based human age estimation by manifold learning and locally adjusted robust regression. IEEE Transactions on Image Processing, 17(7), pp.1178-1188.
23. Wiskott, L., Fellous, J.M., Kuiger, N. and Von Der Malsburg, C., 1997. Face recognition by elastic bunch graph matching. IEEE Transactions on pattern analysis and machine intelligence, 19(7), pp.775-779.
24. Liu, C. and Wechsler, H., 2001. A shape-and texture-based enhanced Fisher classifier for face recognition. IEEE Transactions on Image processing,10(4), pp.598-608.
25. Belhumeur, P.N., Hespanha, J.P. and Kriegman, D.J., 1996, April. Eigenfaces vs. Fisherfaces: Recognition using class specific linear projection. In European Conference on Computer Vision (pp. 43-58). Springer Berlin Heidelberg.
26. Pentland, A., 2000. Looking at people: Sensing for ubiquitous and wearable computing. IEEE Transactions on Pattern analysis and machine intelligence,22(1), pp.107-119.
27. Ojala, T., Pietikainen, M. and Maenpaa, T., 2002. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Transactions on pattern analysis and machine intelligence, 24(7), pp.971-987.
28. Ahonen, T., Hadid, A. and Pietikäinen, M., 2004, May. Face recognition with local binary patterns. In European conference on computer vision (pp. 469-481). Springer Berlin Heidelberg.
29. Liu, C. and Wechsler, H., 2001. A shape-and texture-based enhanced Fisher classifier for face recognition. IEEE Transactions on Image processing,10(4), pp.598-608.
30. Chang, C.C. and Lin, C.J., 2011. LIBSVM: a library for support vector machines. ACM Transactions on Intelligent Systems and Technology (TIST),2(3), p.27.
31. Dibeklioğlu, H., Alnajar, F., Salah, A.A. and Gevers, T., 2015. Combining facial dynamics with appearance for age estimation. IEEE Transactions on Image Processing, 24(6), pp.1928-1943.
32. Weeks, A.R., 1996. Fundamentals of electronic image processing (pp. 316-414). Bellingham: SPIE Optical Engineering Press.
33. Snyers, D. and Pétillot, Y., 1995. Image processing optimization by genetic algorithm with a new coding scheme. Pattern Recognition Letters, 16(8), pp.843-848.
34. Furtado, J.J., Cai, Z. and Xiaobo, L., 2010. Digital Image Processing: Supervised Classification using Genetic Algorithm in MATLAB Toolbox.Report and Opinon, pp.53-61.
