Abstract The traditional locally linear embedding (LLE) algorithm doesn’t take into consideration the class label information of training samples while the supervised LLE (SLLE) algorithm treats the different between classes equally which is inappropriate for facial expression recognition. Taking the differences between expressions into account, a new supervised locally linear embedding (SLLE) algorithm called expression related SLLE (ERSLLE) is designed which uses different weights for sample distance calculation in determining neighborhood samples. The proposed algorithm is applied to the facial expression recognition on the Japanese female facial expression database (JAFFE). The results show that the proposed algorithm is effective and superior to the traditional LLE and SLLE. Better performance is obtained for facial expression recognition in a certain range of the number k of the nearest neighborhood, compared with LLE and SLLE.
[1] Liu Xiaomin, Tan Huachun, Zhang Yujin. New Research Advances in Facial Expression Recognition. Journal of Image and Graphics, 2006, 11(10): 1359-1368 (in Chinese) (刘晓旻,谭华春,章毓晋.人脸表情识别研究的新进展.中国图象图形学报, 2006, 11(10): 1359-1368) [2] Roweis S T, Saul L K. Nonlinear Dimensionality Reduction by Locally Linear Embedding. Science, 2000, 290(5500): 2323-2326 [3] Roweis S T, Saul L K. Think Globally, Fit Locally: Unsupervised Learning of Low Dimensional Manifolds. Journal of Machine Learn- ing Research, 2003, 4: 119-155 [4] de Ridder D, Kouropteva O, Okun O, et al. Supervised Locally Linear Embedding // Proc of the International Conference on Artificial Neural Networks and Neural Information Processing. Istanbul, Turkey, 2003: 333-341 [5] Chang Ya, Feris R, Hu Changbo, et al. Manifold Based Analysis of Facial Expression. Journal of Image and Vision Computing, 2006, 24(6): 605-614 [6] Ma Rui, Wang Jiaxin, Song Yixu. Multi-Manifold Learning Using Locally Linear Embedding (LLE) Nonlinear Dimensionality Reduction. Journal of Tshinghua University: Sciense and Technology, 2008, 48(4): 582-585 (in Chinese) (马 瑞,王家廞,宋亦旭.基于局部线性嵌入(LLE)非线性降维的多流形学习.清华大学学报:自然科学版, 2008, 48(4): 582-585) [7] Wang Hao, Wang Kongqiao. Affective Interaction Based on Person-Independent Facial Expression Space. Neurocomputing, 2008, 71(10/11/12): 1889-1901 [8] Tang Jinghai, Ying Zilu, Zhang Youwei. The Contrast Analysis of Facial Expression Recognition by Human and Computer // Proc of the 8th International Conference on Signal Processing. Guilin, China, 2006, Ⅲ: 1649-1653 [9] Zhu Minghan, Luo Dayong, Yi Liqun, et al. Incremental Locally Linear Embedding Algorithm Based on Orthogonal Iteration Method. Acta Electronica Sinica, 2009, 37(1): 132-136 (in Chinese) (朱明旱,罗大庸,易励群,等.基于正交迭代的增量LLE算法.电子学报, 2009, 37(1): 132-136) [10] Ying Zilu, Fang Xieyan. Combining LBP and Adaboost for Facial Expression Recognition // Proc of the International Conference on Signal Processing. Beijing, China, 2008: 1461-1464
2010, Vol. 23 
