1. ECE 533 Final Project SIMPLE FACE RECOGNITION IMPLEMENTATION FOR COMPUTER AUTHENTICATION Josh Easton- Tin-Yau Lo

2. Goal  Demonstrate the feasibility of computer authentication using facial recognition algorithms

3. What is facial recognition?  Every person’s face has a set of unique characteristics  Some examples are:  Distance between eyes  Location and size of nose  Distance from forehead to chin  Humans are able to easily recognize a face

4. What is computer-based facial recognition?  Programming a computer to use an algorithm to detect if two faces match

5. Facial recognition algorithms  Various computer algorithms exist that can be used to recognize faces  Eigenface analysis (AKA Principal Component Analysis)  Hidden Markov Models

6. Eigenfaces  Computer is trained with several pictures of the same face  Eyes are used as reference point between pictures  Various Eigenvectors are calculated to create a signature of the face

7. Eigenfaces

8. Hidden Markov Model Algorithms  Similar to Eigenfaces  Set of characteristics are stored from a set of images of the same face  The set of images are used to compare if face in another picture matches

9. Embedded HMM for Face Recognition Model- - Face ROI partition

10. Face recognition using Hidden Markov Models One person – one HMM Stage 1 – Train every HMM Stage 2 – Recognition P i - probability Choose max(P i ) … 1 n i

11. Our implementation of computer authentication  Uses Eigenfaces algorithm  Written in Java  “FaceRecognitionCap” - a Quicktime Java program to capture image from a Firewire DV camera or the Apple iSight  A command line program is to simulate authentication with a capture picture and display the closest match.

12. Running the Programs  The distribution came with the directory “FaceRecognitionCap” and “FaceRecognition”.

13. FaceRecognitionCap  Quicktime Java program, that requires Quicktime 6.1 and a compatible camera that support Quicktime on Windows with a simple recompilation.  It runs out of the box on Mac OS X by double- clicking the “FaceRecognitionCap” Icon. Push “Power” to initialize the Firewire bus, and click “Take Snapshot” to produce a 320x240 greyscale image suitable for “FaceRecognition”. The resultant capture file is “test.jpg”

14. FaceRecognition   FaceRecognition is the actual face recognition engine. Type the following at the “FaceRecognition” directory : java FaceRecognition trainedimages testing.jpg   A sample running such as the following will be produced : kenneth% java FaceRecognition trainedimages testing.jpg Constructing face-spaces from trainedimages... Comparing testing.jpg... Most closly reseambling: 15.jpg with 2.108734631580217 distance. kenneth%

15. Why use facial recognition for authentication?  Average computer user has several passwords they must remember  If the user can use their face to authenticate instead, then then will no longer have to remember a password  Saves time currently spent resetting a lost password

16. Conclusion  Facial recognition software is a new, advanced replacement for text passwords  We can look forward to seeing more facial authentication systems in the future