1. FACE RECOGNITION By Vaishali S. Bansal M.Tech Computer C.G.P.I.T. Bardoli

2. LEARNING OBJECTIVES THE VERY BASICS : What is face recognition? Difference between detection & recognition !!! The origin and use of this technology ? What are the various approaches to recognize a face? OUR SELECTED FACE RECOGNITION METHOD : Introduction to PCA Based Eigen Face Recognition Method.

3. WHAT IS FACE RECOGNITION? Face Recognition is the task of identifying an already detected face as a KNOWN or UNKNOWN face, and in more advanced cases, TELLING EXACTLY WHOS IT IS ! FEATUREFACE DETECTION FACE RECOGNITION EXTRACTION

4. METHODS FOR FEATUREEXTRACTION/FACE RECOGNITION

5. FACE DETECTION V/S FRECOGNITION Face database Output: Mr.Chan Face detection Face recognition Prof..Cheng5 face interface v.2a

6. THE "PCA" ALGORITHM

7. STEP 0: Convert image of training set to image vectors A training set consisting of total M images Each image is of size NxN

8. STEP 1: Convert image of training set to image vectors A training set consisting of total M image foreach (image in training set) { 1 Image converted to vector NxN Image N Ti } Vector Free vector space

9. STEP 2: Normalize the face vectors 1. Calculate the average face vectors A training set consisting of total M image Image converted to vector Calculate average face vector U U Ti Free vector space

10. STEP 2: Normalize the face vectors 1. Calculate the average face vectors 2. Subtract avg face vector from each face vector A training set consisting of total M image Image converted to vector Calculate average face vector U U Then subtract mean(average) face Ti vector from EACH face vector to get to get normalized face vector i=Ti-U Free vector space

11. STEP 2: Normalize the face vectors 1. Calculate the average face vectors 2. Subtract avg face vector from each face vector A training set consisting of total M image Image converted to vector i=Ti-U U Eg. a1 m1 Ti a2 m2 1= . . . . Free vector space a3 m3

12. STEP 3: Calculate the Eigenvectors (Eigenvectors represent the variations in the faces ) A training set consisting of total M image Image converted to vector To calculate the eigenvectors , we U need to calculate the covariance vector C Ti C=A.AT where A=[1, 2, 3, M] Free vector space N2 X M

13. STEP 3: Calculate the Eigenvectors A training set consisting of total M image Image converted to vector U C=A.AT Ti N2 X M M X N2 = N2 X N2 Very huge Free vector space matrix

14. STEP 3: Calculate the Eigenvectors A training set consisting of total M image N2 eigenvectors Image converted to vector U C=A.AT Ti N2 X M M X N2 = N2 X N2 Very huge Free vector space matrix

15. STEP 3: Calculate the Eigenvectors A training set consisting of total M image N2 eigenvectors Image converted to vector But we need to find only K U eigenvectors from the above N2 eigenvectors, where K

16. STEP 3: Calculate the Eigenvectors A training set consisting of total M image N2 eigenvectors Image converted to vector SOLUTION U DIMENSIONALITY REDUCTION Ti i.e. Calculate eigenvectors from a covariance of reduced Free vector space dimensionality

17. STEP 4: Calculating eigenvectors from reduced covariance matrix A training set consisting of total M image M2 eigenvectors Image converted to vector New C=AT .A U M XN2 N2 X M = M XM Ti matrix Free vector space

18. STEP 5: Select K best eigenfaces such that K threshold ? UNKNOWN FACE w1 Calculate Distance between = w2 input weight vector and all the : weight vector of training set wk =|i|2 i=1M Weight vector of input image

25. Applications..

26. Thank you