激光杂志
激光雜誌
격광잡지
LASER JOURNAL
2014年
11期
23-25,29
,共4页
人脸识别%提取特征%最小二乘支持向量机%人脸分类器
人臉識彆%提取特徵%最小二乘支持嚮量機%人臉分類器
인검식별%제취특정%최소이승지지향량궤%인검분류기
Face recognition%Extraction features%Least square support vector machine%Face image classifier
为了获得更加理想的人脸识别结果，提高人脸识别正确率，提出一种K近邻和最小二乘支持向量机相融合的人脸识别方法(KNN-LSSVM)。首先采集人脸图像，提取人脸图像特征，并采用KNN删除特征向量中的重复特征，得到人脸图像的特征向量；然后将特征向量输入到最小二乘支持向量机训练，建立相应的人脸分类器；最后采用ORL人脸数据库和Yale人脸库进行仿真实验。仿真结果表明，KNN-LSSVM提高了人脸识别的正确率和识别效率，且具有较强的鲁棒性。
為瞭穫得更加理想的人臉識彆結果，提高人臉識彆正確率，提齣一種K近鄰和最小二乘支持嚮量機相融閤的人臉識彆方法(KNN-LSSVM)。首先採集人臉圖像，提取人臉圖像特徵，併採用KNN刪除特徵嚮量中的重複特徵，得到人臉圖像的特徵嚮量；然後將特徵嚮量輸入到最小二乘支持嚮量機訓練，建立相應的人臉分類器；最後採用ORL人臉數據庫和Yale人臉庫進行倣真實驗。倣真結果錶明，KNN-LSSVM提高瞭人臉識彆的正確率和識彆效率，且具有較彊的魯棒性。
위료획득경가이상적인검식별결과，제고인검식별정학솔，제출일충K근린화최소이승지지향량궤상융합적인검식별방법(KNN-LSSVM)。수선채집인검도상，제취인검도상특정，병채용KNN산제특정향량중적중복특정，득도인검도상적특정향량；연후장특정향량수입도최소이승지지향량궤훈련，건립상응적인검분류기；최후채용ORL인검수거고화Yale인검고진행방진실험。방진결과표명，KNN-LSSVM제고료인검식별적정학솔화식별효솔，차구유교강적로봉성。
In order to obtain good face recognition results and improve the face recognition rate, this paper pro-posed a new face recognition method based on K nearest neighbor and least square support vector machine. Firstly, face images are collected and features are extracted, and the features are selected by K nearest neighbor algorithm, and then the features are input to least square support vector machine to train and established image classifier, finally the simulation experiment is carried out on the ORL face database and Yale face database. The simulation results show that the proposed method has improved the recognition rate and recognition efficiency, and has well robust.
