摘要
针对人耳识别中无法避免的小样本问题,提出了基于Gabor特征和改进LDA(ILDA)的识别算法。该算法首先提取人耳局部Gabor特征,然后重新定义Fisher准则和类内分散度矩阵,再将高维空间映射到低维后寻找最优投影方向,最后利用训练样本与测试样本特征投影值的欧氏距离进行分类识别。与传统方法相比,新算法能有效解决人耳识别中的小样本问题,获得较高的识别准确率。
We propose a novel ear recognition algorithm based on Gabor features and improved LDA to deal with the inevitable problem of small sample size. We firstly extract ear features by the local Gabor filter,and redefine the new Fisher criteria and the intra class scatter matrix. Then we seek the optimal projection direction by mapping from a higher-dimensional space to a lower-dimensional space, Finally we make a comparison of the Euclidean distance of projecting feature vectors between the training samples and the testing samples, and classify them accordingly. Experimental results show that, compared with the traditional methods, the proposed algorithm can effectively solve the small sample size problem in ear recognition with a higher recognition accuracy.
出处
《计算机工程与科学》
CSCD
北大核心
2015年第7期1355-1359,共5页
Computer Engineering & Science
基金
江西省教育厅科技项目(GJJ14430)
江西省教育厅重点项目(赣教技字[12770]号)
