计算机与数字工程2019，Vol.47Issue(1)：226-230,5.DOI:10.3969/j.issn.1672-9722.2019.01.047
基于镜像脸的FLDA单训练样本人脸识别方法∗
FLDA Used for Face Recognition with Single Training Image Per Person Based on Mirror Faces
何刚 1袁秀娟 2张伟 1阎石1
作者信息
- 1. 兰州大学信息科学与工程学院 兰州 730000
- 2. 西北民族大学电气工程学院 兰州 730000
- 折叠
摘要
Abstract
Fisher linear discriminant analysis(FLDA)is a classical face recognition method based on feature extraction. How?ever,it cannot be used when each object has only one training sample because the intra-class variations cannot be statistically mea?sured in this case. In this paper,a novel solution is proposed to this problem by using individual available face image to obtain its mirror face and integrate the original face image and its mirror face image to calculate the intra-class scatter matrix. Then the FLDA algorithm is used to extract the discriminant facial features to achieve the correct classification and recognition. The experimental re?sults show that the proposed method is simple and efficient,and it can achieve higher recognition accuracy than the existing schemes.关键词
人脸识别/FLDA/单训练样本/镜像脸Key words
Key Words face recognition/FLDA/single training sample/mirror face分类
数理科学引用本文复制引用
何刚,袁秀娟,张伟,阎石..基于镜像脸的FLDA单训练样本人脸识别方法∗[J].计算机与数字工程,2019,47(1):226-230,5.
