Three-Dimensional Face Recognition Using Three-Dimensional Face Recognition Using Surface Space Combinations Surface Space Combinations

Thomas Heseltine, Nick Pears, Jim AustinThomas Heseltine, Nick Pears, Jim AustinAdvanced Computer Architecture Group

Department of Computer Science - University of York

www.cs.york.ac.uk/~tomh tom.heseltine@cs.york.ac.uk

2

IntroductionIntroduction

•Face recognition offers several advantages over other biometrics

•Covert operation.

•Human readable media.

•Public acceptance.

•Data required is readily available – police databases etc.

But…

•Growing interest in biometric authentication

•National ID cards, Airport security (MRPs), Surveillance.

•Fingerprint, iris, hand geometry, gait, voice, vein and face.

3

Limitations of 2D Face RecognitionLimitations of 2D Face Recognition

•Lighting conditions.•Different lighting conditions for enrolment and query.•Bright light causing image saturation.

•Head orientation.•2D feature distances appear to distort.

•Image quality.•CCTV, Web-cams etc.

•Facial expression.•Changes in feature location and shape.

•Partial occlusion•Hats, scarves, glasses etc.

System effectiveness is highly dependant on image capture conditions.

Face recognition is not as accurate as other biometrics.

Error rates that are too high for many applications in mind.Result:

4

A Possible Solution…A Possible Solution…3D Face Recognition3D Face Recognition

•Newly emerging 3D cameras allow sub-second generation of 3D face models

•Using 3D face models for recognition potentially provides the following benefits:

•Use of geometric depth information rather than colour and texture

Invariant to lighting conditions

•Ability to rotate face model in 3D space

Invariant to head angle

•3D models captured to scale

Absolute measurements invariant to camera distance

5

3D Face Data3D Face Data•Generated using a stereo vision camera enhanced by light projection.

•Stored in OBJ file format.

•Approximately 8000 points on a facial surface.

•Greyscale texture mapped.

Wire-mesh TexturePolygons Lighting

6

The Fishersurface MethodThe Fishersurface Method•Developed in previous work

•[Heseltine, Pears, Austin. Three-Dimensional Face Recognition: A Fishersurface Approach].

•Adaptation of the fisherface method to 3D face data.•[Belhumeur, Hespanha, Kriegman, Eigenfaces vs. Fisherfaces: Face Recognition using class specific linear projection].

•Uses PCA + LDA to create a surface space projection matrix

Orientate 3D face models to face directly forwards.

Convert to depth-map representation (60 by 90 pixels).

Train on 300 depth maps of 50 different people.

Projected depth maps compared using Euclidean or cosine distance metrics.

7

8

Test DatabaseTest Database•Little publicly available 3D Face data, so we collect our own 3D face database:

•Database now consists of over 5000 face models of over 350 people.

•Large range of expression, orientation, gender, ethnicity, age.

•We take a subset of this database (1770 models) for training and testing.

•300 3D models of 50 people for training

•1470 3D models of 280 people for testing

9

Error RatesError Rates•Error curves produced for all surface representations.

•EER taken as a single comparative value.

•A large range of error rates produced.

10

Surface Space Analysis Using FLDSurface Space Analysis Using FLD

c

i xi

c

ii

i

imx

mmd

1

21

1

2

)(

)( Fisher’s Linear Discriminant calculates the ratio of between-class and within-class scatter, providing an indication of discriminating ability.

11

Combining Surface Space DimensionsCombining Surface Space Dimensions

However, even the worst representations produce a surface space with some highly discriminatory dimensions.

Some surface representations perform better than others.

•Extract “best” dimensions from all surface spaces

•Incorporate into a single combined surface space

•Dividing each element by its within-class standard deviation effectively weights each dimension evenly.

12

Test ProcedureTest Procedure

13

3D Combination Results3D Combination Results

9.3% EER on the blind test set (11.5% single)

8.2% EER on the full test set (11.3% single)

7.2% EER on test set used to calculate dimension combinations (11.6% single)

Face space dimensions are selected from a wide range of systems and combined to form a single unified 3D face space.

Using the cosine metric results in combining more surface space dimensions.

Questions?Questions?

Thomas Heseltine, Nick Pears, Jim AustinThomas Heseltine, Nick Pears, Jim AustinAdvanced Computer Architecture Group

Department of Computer Science - University of York

www.cs.york.ac.uk/~tomh tom.heseltine@cs.york.ac.uk

Three-Dimensional Face Recognition UsingThree-Dimensional Face Recognition UsingSurface Space CombinationsSurface Space Combinations