摘要
提出一种基于分块小波的人脸识别新算法。在充分考虑提取局部特征,又克服小样本问题的基础上,提出分块小波的概念。首先,对小波分解后的低频子图进行分块,提取局部特征,从而降低图像维数并除去冗余噪声;将其先后进行PCA和LDA变换,得到组合特征向量;最后,根据KNN的快速分类能力及SVM在少数类别分类上的优势,提出KNN+SVM融合分类器对组合特征向量进行分类识别。研究结果表明:该方法识别率高,识别速度快,具有一定的实用价值。
A new face recognition based on the blocking wavelet transforms was proposed. To fully extract local features, and overcome the small sample size problem, the concept of block wavelet was presented. First the local characteristics of the face were extracted by the blocking methods after wavelet decomposition of low frequency sub-band. The dimensions were reduced and the redundancy noise was eliminated. After that, the PCA and LDA transformations were taken to obtain the combine eigenvectors. Finally, taking advantage of the fast classification ability of the KNN and the high recognition rate of SVM on a few category classifications, a new blend classifier named KNN＋SVM was constructed to recognize the combined eigenvectors. The results show that the new method has high recognition rate, fast identifying speed, and practical value.
出处
《中南大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2013年第5期1902-1909,共8页
Journal of Central South University:Science and Technology
基金
国家自然科学基金资助项目(61173119)
