基于局部特征的部分遮挡人耳识别

Partially occluded ear recognition based on local features

通过对人耳受到部分遮挡时识别的研究,提出了一种基于局部特征的部分遮挡人耳识别方法,即首先利用Gabor小波对人耳图像进行特征提取,由于该特征维数较高,再使用核Fisher判别分析(KFDA)方法进行有效降维后用于人耳识别.在逐步分析人耳各个子区域的鉴别能力的基础上,提出了基于分块图像和概率模型的识别方法.在北京科技大学(USTB)人耳图像库上的实验结果表明:基于Gabor滤波后图像所提取的特征比基于原始图像直接提取的特征具有更高的识别率,基于分块图像的识别率高于基于整体图像的识别率.

A local feature based approach was proposed for ear recognition under partial occlusion.Firstly,the Gabor filter is applied for feature extraction.Because the Gabor feature vector is of high dimension,kernel Fisher discriminant analysis（KFDA）is used for dimension reduction as well as class separability enhancement.Based on investigations on the different discriminating ability of sub-regions in ear images,a sub-region and probability based model is proposed for recognition.Experimental results on the USTB ear image database show that ear recognition based on the features extracted from Gabor filtered images performs better than that based on the features extracted from the original images,and the local features based strategy gets a higher recognition rate than the whole image based strategy for recognition.