摘要
针对人脸表情识别中Gabor特征向量的高维度信息冗余问题,提出了一个2层Gabor特征选择方法.该方法首先利用改进方差比率作为评估特征的区分能力对高维向量进行过滤,然后对过滤得到的特征子集进行Ad-aBoost特征选择,以挑选出最具区分度的特征,从而降低了Gabor特征的表示维度.实验结果验证了所提方法的有效性,在训练时间和识别性能两者之间取得了较好的平衡.
In order to reduce the curse of dimensionality of Gabor features in facial expression recognition, a two-level feature selection algorithm is developed. Firstly, the original Gabor features are pre-optimized according to the augmented variance ratio to represent the distinguish ability of each feature. Then, the most informative Gabor features are obtained with AdaBoost feature selection algorithm from the preoptimized subset. As a result, the dimension of features is effectively reduced. Experimental results prove the effectiveness of the proposed method, by achieving a balance between training time and recognition rate.
出处
《计算机辅助设计与图形学学报》
EI
CSCD
北大核心
2008年第1期79-84,共6页
Journal of Computer-Aided Design & Computer Graphics
基金
国家自然科学基金(69903006
60373065)
国家"八六三"高技术研究发展计划(2007AA01Z334)
教育部新世纪优秀人才资助计划(NCET-04-0460)
