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Three-Dimensional Face Recognition Using Surface Space Combinations

Three-Dimensional Face Recognition Using Surface Space Combinations. Thomas Heseltine, Nick Pears, Jim Austin Advanced Computer Architecture Group Department of Computer Science - University of York. www.cs. york .ac.uk/~tomh. tom.heseltine@cs.york.ac.uk. Introduction.

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Three-Dimensional Face Recognition Using Surface Space Combinations

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  1. Three-Dimensional Face Recognition Using Surface Space Combinations Thomas Heseltine, Nick Pears, Jim Austin Advanced Computer Architecture Group Department of Computer Science - University of York www.cs.york.ac.uk/~tomh tom.heseltine@cs.york.ac.uk

  2. Introduction • Growing interest in biometric authentication • National ID cards, Airport security (MRPs), Surveillance. • Fingerprint, iris, hand geometry, gait, voice, vein and face. • Face recognition offers several advantages over other biometrics • Covert operation. • Human readable media. • Public acceptance. • Data required is readily available – police databases etc. • But…

  3. Face recognition is not as accurate as other biometrics. Error rates that are too high for many applications in mind. Result: Limitations of 2D Face Recognition System effectiveness is highly dependant on image capture conditions. • 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.

  4. A Possible Solution… 3D 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 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 Polygons Texture Lighting

  6. 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.

  7. Test 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

  8. Error Rates • Error curves produced for all surface representations. • EER taken as a single comparative value. • A large range of error rates produced.

  9. Surface Space Analysis Using FLD Fisher’s Linear Discriminant calculates the ratio of between-class and within-class scatter, providing an indication of discriminating ability.

  10. Combining Surface Space Dimensions Some surface representations perform better than others. However, even the worst representations produce a surface space with some highly discriminatory dimensions. • 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.

  11. Test Procedure

  12. 3D Combination Results 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. 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)

  13. Questions? Three-Dimensional Face Recognition Using Surface Space Combinations Thomas Heseltine, Nick Pears, Jim Austin Advanced Computer Architecture Group Department of Computer Science - University of York www.cs.york.ac.uk/~tomh tom.heseltine@cs.york.ac.uk

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