Application of Facial Recognition in Biometric Security

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Presentation transcript:

Application of Facial Recognition in Biometric Security Kyle Ferris

Introduction 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.

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 testimages, plain photos are taken and used as

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

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

Algorithms (continued) Mean Pixel Area Comparison: 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.

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

Incorporating PCA Right now, the algorithm just runs on an area of a 3x3 pixels. Eventually, would like to be able to recognize critical areas in the images (the Principal Components) and run the algorithm over those areas for the two images

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