Improved LBP based Descriptors in Harsh Illumination Variations For Face Recognition

Shekhar Karanwal
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引用次数: 2

Abstract

Local Binary Pattern (LBP) was considered as one of the prominent local descriptors in the research community. After LBP evolution diverse LBP variants are launched in unconstrained conditions. The main problem with LBP and other descriptors are that in extreme illumination changes their performances are not adequate that's why improvements were suggested and implemented. Precisely proposed work suggests improvements to 3 local descriptors i.e. LBP, Horizontal Elliptical LBP (HELBP) and Median Binary Pattern (MBP). The improvements were suggested by deploying 2 Dimensional-Discrete Wavelet Transform (2D-DWT) prior to feature extraction. After employing 2D-DWT (utilizing haar at level 1), the input image is decomposed into 4 sub-bands. First one signifies approximation coefficient and rest three signifies detail coefficients which are horizontal, vertical and diagonal. Then LBP, HELBP and MBP histograms were separately extracted from 4 wavelet sub-bands. The sub-band histograms were fused for developing the respective descriptor feature length. These 3 improved descriptors are defined as 2D-DWT+LBP, 2D-DWT+HELBP and 2D-DWT+MBP. Fishers Linear Discriminant Analysis (FLDA) was used for reducing the dimension. Then classification was attained by the Radial Basis Function (RBF) (the Support Vector Machines (SVMs) based method). On Yale B (YB) and Extended YB (EYB) datasets, improved descriptors beats the outcome of original descriptors completely.
基于LBP的强光照变化人脸识别改进描述符
局部二值模式(Local Binary Pattern, LBP)是目前研究领域中较为突出的局部描述符之一。LBP进化后,在无约束条件下启动了不同的LBP变体。LBP和其他描述符的主要问题是,在极端光照变化下,它们的性能不够,这就是为什么建议和实施改进的原因。精确提出的工作建议改进3个局部描述符,即LBP,水平椭圆LBP (HELBP)和中位数二进制模式(MBP)。通过在特征提取之前部署二维离散小波变换(2D-DWT),提出了改进建议。使用2D-DWT(利用haar在1级)后,输入图像被分解成4个子带。第一个表示近似系数,其余三个表示细节系数,分别是水平、垂直和对角线。然后分别从4个子带提取LBP、HELBP和MBP直方图。将子带直方图融合以确定各自的描述子特征长度。这3种改进的描述符分别定义为2D-DWT+LBP、2D-DWT+HELBP和2D-DWT+MBP。采用fisher线性判别分析(FLDA)进行降维。然后采用径向基函数(RBF)(基于支持向量机(svm)的方法)进行分类。在Yale B (YB)和Extended YB (EYB)数据集上,改进的描述符完全优于原始描述符的结果。
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