摘要
传统线性子空间算法在提取类内散度矩阵的特征向量时,存在偏差、过拟合和推广能力差的问题。为此,提出一种新的子空间算法。将类内散度矩阵的特征空间分解为2个子解空间,即主成分空间和零空间,再利用本征谱模型对2个空间分别进行正则化。在ORL人脸库上的实验表明,该算法使用较少的特征维数就能达到与传统算法相同的识别率。
For the other line subspace approach existing some problems of bias,overfitting and poor generalization when extracting eigenfeatures from within-class matrix,a new subspace approach is proposed.This approach decomposes the eigenfeature space into two spaces: principal component subspace and zero subspace,and regularizes the two subspaces separately to alleviate the problems of instability,overfitting or poor generalization.Experiments on ORL face base show the method achieves a given recognition rate with fewer features than other approaches and outperforms others.
出处
《计算机工程》
CAS
CSCD
北大核心
2011年第19期148-149,152,共3页
Computer Engineering
基金
国家自然科学基金资助项目(60372049)
江西省科技计划基金资助项目(GJJ09412)
关键词
子空间法
人脸识别
本征谱
特征提取
识别率
subspace method
face recognition
eigenfeature spectrum
feature extraction
recognition rate