3dfacerec-combinedspaces

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Three-Dimensional Face Recognition UsingSurface Space Combinations

Thomas Heseltine, Nick Pears, Jim AustinAdvanced Computer Architecture Group

Department of Computer Science - University of York

www.cs.york.ac.uk/~tomh [email protected]


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Introduction

Face recognition offers several advantages over other biometrics

Covert operation.

Human readable media.

Public acceptance.

Data required is readily availablepolice databases etc.

But

Growing interest in biometric authentication

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

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


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Limitations of 2D Face Recognition

Lighting conditions.

Different lighting conditions for enrolmentand 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:


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3D Face DataGenerated using a stereo vision cameraenhanced by light projection.

Stored in OBJ file format.

Approximately 8000 points on a facial surface.

Greyscale texture mapped.

Wire-mesh TexturePolygons

Lighting


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The 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.


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Test DatabaseLittle 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


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Error Rates

Error curves produced for all surfacerepresentations.

EER taken as a single comparative

value.

A large range of error rates produced.


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Surface Space Analysis Using FLD

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mx

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)( Fishers Linear Discriminant calculates the ratio of

between-class and within-class scatter, providing

an indication of discriminating ability.


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Combining Surface Space Dimensions

However, even the worst representations

produce a surface space with some highly

discriminatory dimensions.

Some surface representationsperform 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.


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Test Procedure


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Questions?

Thomas Heseltine, Nick Pears, Jim Austin

Advanced Computer Architecture Group

Department of Computer Science - University of York

www.cs.york.ac.uk/~tomh [email protected]

Three-Dimensional Face Recognition Using

Surface Space Combinations

3dfacerec-combinedspaces

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