html5-img
1 / 13

ECE 533 Final Project

ECE 533 Final Project. SIMPLE FACE RECOGNITION IMPLEMENTATION FOR COMPUTER AUTHENTICATION. Josh Easton - Tin-Yau Lo. Goal. Demonstrate the feasibility of computer authentication using facial recognition algorithms. What is facial recognition?.

boaz
Télécharger la présentation

ECE 533 Final Project

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

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

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

  10. Running the Programs • The distribution came with the directory “FaceRecognitionCap” and “FaceRecognition”.

  11. 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”

  12. 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%

  13. 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

More Related