1. Septian Adi Wijaya – Informatics Brawijaya University
PERBANDINGAN METODE PENGENALAN WAJAH SECARA REAL-TIME PADA PERANGKAT BERGERAK BERBASIS ANDROID
Septian Adi Wijaya – Informatics Brawijaya University

2. Main Idea To implemented face recognition application on smart-phone
Doing recognition with 3 different method
Compare accuracy rate of 3 face recognition’s method
Main Idea

3. Introduction & Motivation
Why Face Recognition?
Motivation for using LBPH, PCA and LDA
Introduction & Motivation

4. Two modes of Face Recognition
IDENTIFICATION
To recognize “who is X?”
Executed by system with compared a “one-to- many” search
VERIFICATION
To answer question “is this X?”
Executed by system with compared a “one-to- one” search
Two modes of Face Recognition

5. Five Step Process

6. What’s the different?
LBPH
LBP
LBPH Method

7. Facial Representation Using LBPH
Image divided into many region
Each pixel of region transformed into binary number by circular extended-LBP
Binary number that produced then transformed again into decimal
Decimal number then be next center of pixel & produce histogram (bin range 0-255)
Facial Representation Using LBPH

8. Facial Representation Using LBPH
Calculate total bins of histogram then subtracted to another.
the smallest value result of subtracted then it’ll classify to it’s label image.
*note: get sum histogram of image,
then subtract it with other
= …?
Facial Representation Using LBPH

9. Extended-LBP Extend value of radius and sample point
P = Sample Point, R = Radius
in my paper used P=16 & R=2
and divided 16x16 region
Extended-LBP

10. f PCA Method Treat pixel as a vector
Compute mean on image with configure principal component then represent with matrix f
PCA Method

11. PCA Algorithm Compute covarian matrix
Compute eigenvalue and eigenvector vi from S
Sorting to the largest of eigenvalue  eigenvector
Compute with Euclidean distance to classify image
PCA Algorithm

12. LDA Method Focused on dimensional reduction
Algorithm on LDA almost resemble with PCA
Calculation on mean computed per-class
LDA Method

13. Facial Representation Using LDA
Compute mean total of all class
Compute mean each class
*note: N is number various of class
yi is column vector
*note: Yi is number of class
yi is column vector
Facial Representation Using LDA

14. Looking for the largest matrix each class
LDA Algorithm

15. LDA Algorithm Compute eigenvalue.
Next step almost same on PCA algorithm
LDA Algorithm

16. Methodology

17. Based on testing step that have done with 75 training image (@class 25 image)
25 testing image with time range 5s, 10s, 15s and 20s
Result

18. Accuracy Rate

19. The larger range time for testing image the larger accuracy rate we get
LBPH method is the most suitable for computation on mobile android
Conclusion

20. Thanks…