为了提取具有鉴别能力的红外人脸图像局部结构特征,提出一种基于LBP(local binary pattern)鉴别模式的红外人脸识别方法。传统的LBP均匀模式,提取自然图像中占主导地位的信息用于识别,但占主导地位的信息不一定是最适合识别的。为了提取有效的鉴别模式特征,基于监督学习的思想,在LBP模式下引入可分性标准,对不同LBP模式进行有效的模式选择,从而抽取适合识别的鉴别模式。最后,为了利用人脸的空间位置信息,结合分块和直方图技术得到最后的识别特征。实验结果表明,本文鉴别模式可以提取更适合识别的特征,识别性能优于传统的基于均匀模式的LBP方法。

To extract the discriminant local structural features,an improved infrared face recognition method based on LBP discrimination patterns is proposed in this paper.In traditional uniform patterns of LBP,the most frequency pattern information in nature image is chosen for image recognition.However,the most frequency patterns are not most suitable for face recognition.Based on supervised learn idea,pattern selection algorithm is proposed to get the LBP patterns which are most suitable for infrared face recognition.To make full use of the space locations information,the partitioning and LBP histogram are applied to get final features.The experimental results demonstrate the infrared face recognition method based on LBP and discrimination patterns proposed outperforms the traditional methods based on LBP or PCA.