ECE 533 Final ProjectECE 533 Final ProjectSIMPLE FACE RECOGNITION SIMPLE FACE RECOGNITION

IMPLEMENTATION FOR IMPLEMENTATION FOR COMPUTER AUTHENTICATIONCOMPUTER AUTHENTICATION

Josh Easton - Tin-Yau Lo

GoalGoal

Demonstrate the feasibility of Demonstrate the feasibility of computer authentication using facial computer authentication using facial recognition algorithmsrecognition algorithms

What is facial recognition?What is facial recognition?

Every person’s face has a set of unique Every person’s face has a set of unique characteristicscharacteristics

Some examples are:Some examples are: Distance between eyesDistance between eyes Location and size of noseLocation and size of nose Distance from forehead to chinDistance from forehead to chin

Humans are able to easily recognize a faceHumans are able to easily recognize a face

What is computer-based facial What is computer-based facial recognition?recognition?

Programming a computer to use Programming a computer to use an algorithm to detect if two an algorithm to detect if two faces matchfaces match

Facial recognition algorithmsFacial recognition algorithms

Various computer algorithms exist Various computer algorithms exist that can be used to recognize facesthat can be used to recognize faces Eigenface analysis (AKA Principal Eigenface analysis (AKA Principal

Component Analysis)Component Analysis) Hidden Markov ModelsHidden Markov Models

EigenfacesEigenfaces

Computer is trained with several pictures Computer is trained with several pictures of the same faceof the same face

Eyes are used as reference point between Eyes are used as reference point between picturespictures

Various Eigenvectors are calculated to Various Eigenvectors are calculated to create a signature of the facecreate a signature of the face

EigenfacesEigenfaces

Embedded HMM Embedded HMM for Face for Face

RecognitionRecognition

Model-

- Face ROI partition

Face recognition Face recognition using Hidden Markov Modelsusing Hidden Markov Models

One person – one HMMStage 1 – Train every HMM

Stage 2 – Recognition

Pi - probability

Choose max(Pi)

…1

n

i

Running the ProgramsRunning the Programs

The distribution came with the directory The distribution came with the directory “FaceRecognitionCap” and “FaceRecognitionCap” and “FaceRecognition”.“FaceRecognition”.

FaceRecognitionCapFaceRecognitionCap

Quicktime Java program, that requires Quicktime Java program, that requires Quicktime 6.1 and a compatible camera that Quicktime 6.1 and a compatible camera that support Quicktime on Windows with a simple support Quicktime on Windows with a simple recompilation. recompilation.

It runs out of the box on Mac OS X by double-It runs out of the box on Mac OS X by double-clicking the “FaceRecognitionCap” Icon. Push clicking the “FaceRecognitionCap” Icon. Push “Power” to initialize the Firewire bus, and click “Power” to initialize the Firewire bus, and click “Take Snapshot” to produce a 320x240 “Take Snapshot” to produce a 320x240 greyscale image suitable for “FaceRecognition”. greyscale image suitable for “FaceRecognition”. The resultant capture file is “test.jpg”The resultant capture file is “test.jpg”

FaceRecognitionFaceRecognition

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.jpgConstructing face-spaces from trainedimages ...Comparing testing.jpg ...Most closly reseambling: 15.jpg with 2.108734631580217 distance.kenneth%

ConclusionConclusion

Facial recognition software is a new, Facial recognition software is a new, advanced replacement for text advanced replacement for text passwordspasswords

We can look forward to seeing more We can look forward to seeing more facial authentication systems in the facial authentication systems in the futurefuture