基于血流图和Fisher线性鉴别分析的红外人脸识别方法

Infrared Face Recognition Based on Blood Perfusion and Fisher Linear Discrimination Analysis

为了从生物特征和统计角度来提高识别的性能,提出了一种基于血流图和Fisher线性鉴别(FLD)相结合的人脸识别方法,该方法首先利用血流模型把红外温谱图转换成血流图,能够利用人体的生物特征增加样本之间的类间距,并减少样本之间类内距,然后从统计特性出发,对血流图进行能最大化类间距和类内距的Fisher线性分析法。实验证明,血流图增加类间距和类内距的比值(RD),本方法得到了较好的识别结果。

To get the good performance of infrared face recognition from the biological feature and statistical character,a novel method for infrared face recognition based on blood perfusion and Fisher linear optimal discrimination is proposed in this paper.Firstly,thermal images are converted into blood perfusion domain by blood perfusion model to enlarge between-class distance and lessen within-class distance,which makes full use of the biological feature of the human face.Then,the FLD is chosen to maximize the ratio of between-class distance and within-class distance（RD） from the statistical scope.The experiments illustrate that transformation from thermal images to blood perfusion domain can enlarge the ratio of between-class distance and within-class distance（RD） and the method proposed in this paper has better performance compared with traditional methods.