计算机与数字工程2018，Vol.46Issue(2)：371-374,4.DOI:10.3969/j.issn.1672-9722.2018.02.032
基于LBP采样学习的人脸识别研究
Face Recognition Based on LBP Sampling Learning
耿渊哲1
作者信息
- 1. 南京理工大学计算机科学与工程学院 南京210094
- 折叠
摘要
Abstract
Local binary patterns(LBP)is an efficient local feature and has been widely used in many face recognition systems due to its strong discriminative power and excellent robustness.However,most existing LBP-like face descriptors are hand-crafted, which require strong prior knowledge to engineer them.The present work proposes a learning-based sampling pattern.It could mea-sure the distance of two images using pixel difference vectors(PDV).Moreover,the objective function based on Fisher's linear dis-criminant is established.It should be noticed that the solving problem of this model into a simple 0-1 programming is converted,so as to get the optimal solution in a short time.Extensive experiments on FERET face databases validate the effectiveness of the pro-posed algorithm.关键词
人脸识别/局部二值模式/特征提取/Fisher线性判别Key words
face recognition/local binary patterns/feature extraction/fisher's linear discriminant分类
信息技术与安全科学引用本文复制引用
耿渊哲..基于LBP采样学习的人脸识别研究[J].计算机与数字工程,2018,46(2):371-374,4.
