面向脸部表情识别的Gabor特征选择方法

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AdaBoost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.

Optimal Gabor Feature for Facial Expression Recognition

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.