安阳工学院学报
安暘工學院學報
안양공학원학보
JOURNAL OF ANYANG INSTITUTE OF TECHNOLOGY
2015年
2期
59-61
,共3页
人脸识别%Fisher准则%线性判别%线性回归分类%K-近邻分类器
人臉識彆%Fisher準則%線性判彆%線性迴歸分類%K-近鄰分類器
인검식별%Fisher준칙%선성판별%선성회귀분류%K-근린분류기
face recognition%fisher criterion%linear discriminant%linear regression classification%K-nearest neighbor classifier
为了提高线性回归分类(LRC)算法的鲁棒性,提出了一种基于Fisher准则的线性判别回归分类算法。利用Fisher准则将类间与类内重建误差的比值最大化,找到线性回归分类的最优投影矩阵;再将训练图像及测试图像投影到各类的特征子空间;求得各训练图像与测试图像间的欧氏距离,最后用K-近邻分类器完成人脸识别。在AR人脸数据库上的实验结果表明,相比其他回归分类算法,算法取得了更好的识别效果。
為瞭提高線性迴歸分類(LRC)算法的魯棒性,提齣瞭一種基于Fisher準則的線性判彆迴歸分類算法。利用Fisher準則將類間與類內重建誤差的比值最大化,找到線性迴歸分類的最優投影矩陣;再將訓練圖像及測試圖像投影到各類的特徵子空間;求得各訓練圖像與測試圖像間的歐氏距離,最後用K-近鄰分類器完成人臉識彆。在AR人臉數據庫上的實驗結果錶明,相比其他迴歸分類算法,算法取得瞭更好的識彆效果。
위료제고선성회귀분류(LRC)산법적로봉성,제출료일충기우Fisher준칙적선성판별회귀분류산법。이용Fisher준칙장류간여류내중건오차적비치최대화,조도선성회귀분류적최우투영구진;재장훈련도상급측시도상투영도각류적특정자공간;구득각훈련도상여측시도상간적구씨거리,최후용K-근린분류기완성인검식별。재AR인검수거고상적실험결과표명,상비기타회귀분류산법,산법취득료경호적식별효과。
To improve the robustness of the linear regression classification (LRC) algorithm, a linear discrimi?nant regression classification algorithm based on Fisher criterion is proposed. The ratio of the between-class re?construction error over the within-class reconstruction error is maximized by Fisher criterion so as to find an opti?mal projection matrix for the LRC. Then, all testing and training images are projected to each subspace by the op?timal projection matrix and Euclidean distances between testing image and all training images are computed. Fi?nally, K-nearest neighbor classifier is used to finish face recognition . Experimental results on AR face databases show that proposed method has better recognition effects than several other regression classification approaches.