基于局部二元模式的快速红外人脸识别系统

红外人脸成像具有对光照、人脸皮肤、表情、姿态等因素变化不敏感的特点，可以在一定程度上弥补这些因素对可见光人脸识别影响的不足。为了充分提取红外人的局部鉴别特征，文中提出了一个基于局部二元模式的快速红外人脸识别系统。该系统首先通过thermoVision A40型红外热像仪获分辨率为320240的红外人脸图像，并通过人脸检测和归一化方法提取大小为6080的标准红外人脸图像。其次，基于人脸图像的对称性，将红外人脸图像分块。通过局部二元模式直方图提取每一分块所包含的纹理模式特征。最后，采用Kruskal-Wallis（KW）特征选择算法，进一步抽取对识别有贡献的局部纹理特征用于分类识别。实验结果表明:提出的热红外人脸系统识别率明显优于基于主成分分析（PCA）和线性鉴别分析（LDA）的传统红外人脸识别系统，可以达到98.6%的识别率。与此同时，提出的红外人脸识别系统识别速度也快于传统基于PCA和LDA系统，可以广泛应用于实时人脸识别中。

Fast thermal infrared face recognition system based on local binary pattern

Infrared face recognition, being light- independent, and not vulnerable to facial skin, expressions and posture, can avoid or limit the drawbacks of face recognition in visible light. In this paper, a fast infrared face recognition system based on local features was proposed. Firstly, the original IR images captured by an IR camera thermoVision A40 were 320240 pixels. The sensitivity was as high as 0.08 ℃, the face image was normalized to size of 6080 by preprocessing and face detection. Secondly, based on the symmetry of face, the whole infrared face image was divided into small sub-blocks. To make full use of the local features of infrared face image, a local binary pattern (LBP) was chosen to get the composition of micropatterns of sub-blocks. Finally, Kruskal-Wallis (KW) feature selection method was proposed to improve the effectiveness of discriminant feature extraction. The experiment results illustrate that the system recognition rate can reach 98.6%, outperform the traditional methods based on Principal Component Analysis(PCA) and Linear Discriminant Analysis (LDA). Furthermore, the speed of proposed system is much faster than the traditional methods and can be used in real-time face recognition system.