Application of Facial Recognition in Biometric Security Kyle Ferris

1. Application of Facial Recognition in Biometric Security Kyle Ferris

2. The purpose of this project is to be able to create a "key" for any person who wishes to use the program. An image of the client's face will be taken and used as the base biometric key. The program should be able to recognize the client and authorize him or her, while denying access to those not recognized. Introduction

3. Input Method • A webcam will take a short video of the user • The frames of this video will be converted to image files • To acquire test images, plain photos are taken and used as input

4. Example Input

5. PCA There are many different approaches to this type of problem. One of the most effective is Principal Component Analysis (PCA). PCA involves identifying the principal features of a face (aka, eyes, mouth, jaw structure) and comparing these features for different images

6. Algorithms • Conversion • Converts a .ppm format file, which is a color image, into a specially formatted .pgm format. • Pixel Subtraction: • Creates a third image which shows the obvious differences between two very similar images, with no analysis

7. Algorithms (continued) • Mean Pixel Area Comparison (MPAC): • Averages the pixel intensity in a specified area and compares that value to the average value in the same area on a second image. • Interference Evaluation: • Evaluates how much interference or errant objects are in the image and tells you how reliable the results are.

8. Algorithms (continued) • Principal Components Labeling • Evaluates an image and determines groups of pixels which are stored as components • Uses aspects of edge detection and floodfill

9. Example Output • The “average difference between images” is the example of the Mean Pixel Area Comparison function • You can see the interference level here as well, and an evaluation from the program

10. Incorporating Principal Component Analysis • By default, the program runs by comparing 3x3 blocks of pixels. • For more ideal results, instead of evaluating the similarity of 3x3 blocks, identify the primary components of interest in the images, then compare across those. • This is known as Principal Component Analysis.

11. Connected Components Labeling • Also known as CCL, this is a method to identify which pixels in an image are connected. • Algorithm returns groups of pixels in the form of arrays of their location.

12. Aspects of CCL • Binary filtering method • Filter the image into black and white pixels only based on some arbitrary threshold

13. Aspects of CCL (continued) • From this black and white image, run a recursive method that finds groups of connected pixels • Store these groups as arrays of pixel locations

14. Incorporating CCL with MPAC • When the MPAC method is run on these groups, it gives better results than when run on just square blocks. • This overall process is Principal Component Analysis.

15. Results/Expectations • Will be able to be implemented into any security system with fairly basic equipment. • Able to accurately recognize the user 90% of the time • No false positives- very important to avoid giving authorization to those unauthorized