Facial Recognition

Justin Kwong

Megan Thompson

Raymundo Vazquez-lugo

Why Facial Recognition?

• Does not depend on consent

• Ease of use for security

• Contact-free recognition

• Able to use existing databases and technology

Applications

• Missing Children Identification• Criminal Identification• Passports/Driver’s License • Voter identification• Welfare fraud • Logging on to computer• Accessing files• Surveillance • Access to Building

Problem StatementIdentification:Given a stored database of faces, identify an unknown

input face through facial recognition techniques.

Verification:Given a stored database of faces, confirm or reject the

claimed identity of the input face through facial recognition techniques.

Additional Information:Facial recognition techniques may be enhanced by taking

into consideration other factors such as gender, race, and age.

Issues

• Lighting• Angles• Apparel• Resolution• Background Noise• Movement• Facial Expression• Segmentation (in a scene)

Recognition Techniques

• Correlation

• Eigenfaces

• Linear Subspaces

• Fisherfaces

• 3-D

Correlation

• Nearest Neighbor classifier

• Normalize image mean and and make unit variance

• Correlate images

• Heavy computation

• Subject to all previous issues (lighting, angles, etc.)

Eigenfaces

• Uses a Principal Component Analysis (PCA) Transform

• Uses subspace that maximizes scatter

• Reduces information

• Retains illumination affects

• Does not necessarily separate classes

Linear Subspaces

• Assumes face is a Lambertian surface

• Solve for albedo and surface normal (photometric stereo)

• Insensitive to lighting conditions (but not shadows)

• Computationally expensive to measure variability in class

Fishfaces

• Linear Discriminate Analysis (LDA)

• Uses a learning set

• Maximizes scatter between classes

• Insensitive to illumination

3-D

• 2-D methods are sensitive to external factors such as illumination, head pose, and are also sensitive to the use of cosmetics

• 3D facial recognition uses geometry of the face for accurate identification of the subject

• Yet, the problem of facial expressions is a major issue in 3D face recognition, since the geometry of the face significantly changes as the result of facial expressions

• The main technological limitation of 3D face recognition methods is the acquisition of 3D images, which usually requires a range camera

• In [5] is developed an expression-invariant 3D face recognition approach based on the isometric model of facial expressions

Test Imagery

• Color FERET Database (controlled setup)

• Yale Face Database (numerous variations on single class)

• Harvard Database

• http://www.face-rec.org/databases/

Preliminary Method Choice - Fisherfaces

• LDA useful outside of face rec. problem

• Theoretically insensitive to many issues (e.g. expression, lighting, etc.)

• Previous papers show lowest %error

• Most databases not setup for 3-D image creation

Literature

• www.face-rec.org• Belhumeur, Peter; Hespanha, Joao; Kriegman, David. Eigenfaces vs.

Fisherfaces: Recognition Using Class Specific Linear Projection. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol 19, No. 7. July 1997

• A. Bronstein, M. Bronstein, and R. Kimmel. Expression-invariant 3D face recognition, Proc. Audio & Video-based Biometric Person Authentication (AVBPA), Lecture Notes in Comp. Science 2688, Springer, 2003, pp. 62-69

• Duda, Richard O., Hart, Peter E., Stork, David G. Patter Classification. Second Edition. John Wiley & Sons, Inc.

• Gross, Ralph. Shi, Jianbo. Cohn, Jeff. Quo Vadis Face Recognition? Robotics Institute. Carnegie Mellon University

• Torres, Luis. Is there any hope for face recognition?. Technical University of Catalonia. Barcelona, Spain

• Zhao, W.; Face Recognition: A Literature Survey. National Institute of Standards and Technology.