Evaluation of face recognition techniques using PCA, wavelets and SVM Ergun Gumus, Niyazi Kilic, Ahmet Sertbas, Osman N. Ucan Expert Sysetems with Applications.2010 Elsevier Ltd. All rights reserved. 1 指導老師 : 鄭文昌 報告者 : 許祐松

Outline Introduction Feature extraction methods Classification methods Experiment results Conclusion Personal remark 2

1.Introduction 人臉偵測的應用在日常生活中越來越廣泛 ◦ 機場安全監控 ◦ 公司的門禁系統 ◦ 犯罪偵查 一般人臉偵測分成兩個步驟 ◦ 特徵抽取 Feature extraction ◦ 辨識 Classification 3

1.Introduction 4 DatasetFeature extractionClassificationFace recognition

1.Introduction Feature extraction ◦ 目的 : 特徵抽取用來縮小龐大的輸入資料 ◦ PCA ◦ Eigenfaces method ◦ Wavelet decomposer Classifier ◦ 目的 : 找出最有可能被注意到的特徵 ◦ SVM ◦ Nearest Distance criterion 5

2.Feature extraction methods  Eigenfaces method  Eigenfaces are a set of eigenvectors used in the computer vision problem of human face recognition. 6

2.Feature extraction methods  Eigenfaces method  We have N face images with m rows and m columns.  represent images in column vectors.  1.  2. 7 Mean face image images in column vectors

2.Feature extraction methods  Eigenfaces method  3.  To avoid the complexity, choose C in (NXN)  4. 8 D 差值擺在一 起 C 共變異矩陣

2.Feature extraction methods  Eigenfaces method  5.  6. 9 VEigenvector set veigenvectors UEigenface space WFeature vectors

2.Feature extraction methods  Eigenfaces method 10

2.Feature extraction methods  Wavelet transform method 11

2.Feature extraction methods  Wavelet transform method  1.  aapproximationvvertical hhorizontalddiagonal

3.Classification methods methods  Support Vector Machines(SVM) method SVM is a classification method that aims to separate two data sets with maximum distance between them. 13

3.Classification methods methods  Support Vector Machines(SVM) method 14

3.Classification methods methods  Support Vector Machines(SVM) method d:distence between support vectors 為同時滿足 2.3. 條件，使用 Lagrange function 15

3.Classification methods methods  Support Vector Machines(SVM) method  4.  5. 16

3.Classification methods methods 17

3.Classification methods methods  Support Vector Machines(SVM) method  Polynomial Kernel Function:  RBF Kernel Function: 18

3.Classification methods methods  Nearest Distance criterion  The training image with the minimum Dist(i) value is the most similar one to the test image. 19

4.Experimental results 20

4.Experimental results 21 1/(360) training images / test images

5.Conclusion  Feature extraction methods:  PCA, Wavelets  Classification step:  SVM(three types of kernels),nearest distance  We obtained highest recognition rate as 98.1% with Wavelet SVM(Quadratic polynomial kernel) method.  Male (89.38%) v.s. females(81.41%) 22

6.Personal remark  Use different data sets.  95.3% ??? 23

Thanks for your attention. 24