1 / 14

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.

etana
Download Presentation

Three-Dimensional Face Recognition Using Surface Space Combinations

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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

More Related