计算机工程与应用
計算機工程與應用
계산궤공정여응용
COMPUTER ENGINEERING AND APPLICATIONS
2015年
11期
175-178
,共4页
掌纹识别%核主成分分析%欧式距离%支持向量机%特征提取
掌紋識彆%覈主成分分析%歐式距離%支持嚮量機%特徵提取
장문식별%핵주성분분석%구식거리%지지향량궤%특정제취
palmprint recognition%kernel principal component analysis%Euclidean distance%support vector machine%fea-ture extraction
针对单一PCA或PCA只能提取掌纹的线性或非线性特征，单一分类器的掌纹识别率低缺陷，提出一种子空间特征融合的两级掌纹识别方法（PCA-KPCA-SVM）。首先采用子空间特征提取方法PCA、KPCA分别提取掌纹图像线性和非线性特征，然后基于融合特征总类间距离最大准则，计算出最佳的融合系数，得到PCA、KPCA的融合掌纹特征，最后将融合特征输入到欧式距离分类器进行掌纹识别，如果拒绝识别，则输入支持向量机进行二次识别。采用Polyu掌纹图像库进行测试实验，结果表明，相对于对比算法，PCA-KPCA-SVM提高了掌纹识别率，有效降低了掌纹的误识率和拒识率。
針對單一PCA或PCA隻能提取掌紋的線性或非線性特徵，單一分類器的掌紋識彆率低缺陷，提齣一種子空間特徵融閤的兩級掌紋識彆方法（PCA-KPCA-SVM）。首先採用子空間特徵提取方法PCA、KPCA分彆提取掌紋圖像線性和非線性特徵，然後基于融閤特徵總類間距離最大準則，計算齣最佳的融閤繫數，得到PCA、KPCA的融閤掌紋特徵，最後將融閤特徵輸入到歐式距離分類器進行掌紋識彆，如果拒絕識彆，則輸入支持嚮量機進行二次識彆。採用Polyu掌紋圖像庫進行測試實驗，結果錶明，相對于對比算法，PCA-KPCA-SVM提高瞭掌紋識彆率，有效降低瞭掌紋的誤識率和拒識率。
침대단일PCA혹PCA지능제취장문적선성혹비선성특정，단일분류기적장문식별솔저결함，제출일충자공간특정융합적량급장문식별방법（PCA-KPCA-SVM）。수선채용자공간특정제취방법PCA、KPCA분별제취장문도상선성화비선성특정，연후기우융합특정총류간거리최대준칙，계산출최가적융합계수，득도PCA、KPCA적융합장문특정，최후장융합특정수입도구식거리분류기진행장문식별，여과거절식별，칙수입지지향량궤진행이차식별。채용Polyu장문도상고진행측시실험，결과표명，상대우대비산법，PCA-KPCA-SVM제고료장문식별솔，유효강저료장문적오식솔화거식솔。
Principal Component Analysis(PCA)or Kernel Principal Component Analysis(KPCA)can only extract the linear or nonlinear features of palmprint, and single classifier recognition rate is very low, this paper proposes a two level classifier for palmprint recognition based on subspace features. Firstly, the PCA and KPCA are used to extract the linear or nonlinear features of palmprint, respectively, and the best fusion coefficient can be calculated by making the total distance of between-classes largest to get the optimal features of palmprint image, the Euclidean distance metric method is used to recognize palmprint image, if the palmprint image category is clearly, the recognition result is obtained, otherwise the palmprint image is put into support vector machine to recognize. Polyu palmprint image library is used to test the perfor-mance, the results show that, compared with other palmprint recognition methods, the proposed method has improved the palmprint recognition rate and recognition speed, and false accept rate and false reject rate are reduced.
