Second-order Local Derivative Pattern (LDP) is a general framework to encode directional pattern features based on local first-order derivative variations. Different from Local Binary Pattern (LBP), the second-order derivative pattern extracts local information by encoding various distinctive spatial relationships contained in a given local region and thus it can capture more detailed information than the first-order local pattern used in LBP. A new algorithm based on Fusion of Second-order Local Derivative Pattern in Gabor characteristic (FG2LDP) for face recognition is proposed. According to the good spatial position and orientation of Gabor filter, a Gabor filter with four frequencies and six orientations is firstly applied to filter face images. Secondly, the weight of each frequency and orientation is adaptively estimated for subsequent fusion. Thirdly, the second-order local derivative information of filtered images is extracted and low dimensional features in every direction are extracted by Principal Component Analysis (PCA). Finally, all the likelihoods in every frequency and orientation are fused together for the final recognition result. Experiments show that our method can effectively extract local features. It can consistently outperform other recognition methods based on PCA, Linear Discriminant Analysis (LDA), LBP and Fusion of Second-order Local Derivative Pattern (F2LDP) in gray images.%二阶局部导数模式是一种基于一阶局部导数变化的方向性编码方式.相对于局部二值模式,二阶局部导数模式能够提取出图像的更多细节信息.本文提出了一种融合Gabor特征二阶局部导数模式的人脸识别方法,该方法首先利用Gabor滤波良好的空间位置与方向选择特性,采用四个频率六个方向的Gabor滤波器对图像进行滤波.其次,利用数据的类信息和邻接点信息,自适应地计算各频率和方向的权重,作为后续融合依据.然后,提取Gabor滤波图像四个方向的二阶局部导数特征,采用主成分分析方法对各方向的二阶导数特征进行降维.最后,在识别过程中,结合权重信息融合各方向和频率的识别概率得出最终识别结果.实验结果表明本文算法能够有效 地提取图像细节信息,较其他方法如主成分分析方法,线性判别式方法,局部二值模式算法和融合灰度二阶局部导数模式算法具有更好的识别性能.
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