基于Gabor特征融合与LBP直方图的人脸表情特征提取方法

针对Gabor特征全局表征能力弱以及特征数据维数存在冗余的问题,提出了一种采用Gabor多方向特征融合与分块直方图相结合的方法以有效提取表情特征.通过对不同表情的重要特征部位进行细化,采用Gabor滤波器有针对性地提取相关区域的多尺度和多方向特征,并对同尺度的特征进行融合,利用各区域内融合特征的直方图分布来表征图像.该方法可以提高特征提取的准确性,有效突出重要特征的辨识作用,大幅度降低特征的维数,在JAFFE表情库可以达到100%的识别率.

Extraction method for facial expression features based on Gabor feature fusion and LBP histogram

In order to solve the problem that the Gabor features exhiblt weak global expression ability and redundant feature data dimensionalities, a method in combination with both Gabor multi direction feature fusion and block histogram was proposed to effectively extract the facial expression features. Through refining the important featute parts in different expressions, the multi scale and multi direction features in relevant areas were purposefully extracted with Gabor filters, and the features with the same scale were fused. In addition, the images were characterized with the histogram distribution for the fused features in various areas. The proposed method can improve the accuracy of feature extraction, effectively highlight the identification effect of important features, and greatly reduce the feature dimensionalities. Furthermore, the proposed method can reach 100% recognition rate in the JAFFE expression dataset.