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Nose as a Biometric

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  • 1. The Nose on Your Face May Not be so Plain: Using the Nose as a Biometric Adrian Moorhouse and Adrian EvansDepartment of Electronic & Electrical Engineering, University of Bath Gary Atkinson, Jiuai Sun and Melvyn Smith Machine Vision Laboratory, University of the West of England IET 3rd International Conference on Imaging for Crime Detection and Prevention (ICDP-09), Kingston, UK, 3rd Dec 2009
  • 2. Overview• Introduction• Photometric stereo image acquisition• Nose feature extraction – Nose region segmentation – Curvature-based landmark extraction – Geometric ratio and ridge features• Evaluation of classification performance• Discussion and conclusions IET 3rd International Conference on Imaging for Crime Detection and Prevention (ICDP-09), Kingston, UK, 3rd Dec 2009
  • 3. Introduction• A number of face-based biometrics have been proposed e.g. iris, ear and retina B. Griaule, “Understanding Biometrics”, Online, 2008.• The nose is hard to conceal and relatively invariant to expression• Provides fixation points for face recognition IET 3rd International Conference on Imaging for Crime Detection and Prevention (ICDP-09), Kingston, UK, 3rd Dec 2009
  • 4. Introduction• Noses can be divided into 6 types Nasion• Full 3D nose matching is computationally expensive• Shape of the ridge important IET 3rd International Conference on Imaging for Crime Detection and Prevention (ICDP-09), Kingston, UK, 3rd Dec 2009
  • 5. Photometric stereo image capture• Capture made using PhotoFace system (UWE) – 4 flashguns and 1 200 fps camera – 4 fames captured in ~20 ms Input images Bump map Surface normals• Albedo image unaffected by lighting IET 3rd International Conference on Imaging for Crime Detection and Prevention (ICDP-09), Kingston, UK, 3rd Dec 2009
  • 6. Nose feature extraction• Nose region segmentation – Multistage classifier for face recognition – Skin colour used to remove non-face skin pixels – Nose tip is the closest object to camera – Nose region proportional to output of face recognition stage IET 3rd International Conference on Imaging for Crime Detection and Prevention (ICDP-09), Kingston, UK, 3rd Dec 2009
  • 7. Nose feature extraction• Curvature-based landmark detection – Applied to the surface normals image – Robust mechanism for identifying nasal landmarks – Principle curvatures κmin and κmax found via mean (H) and Gaussian (K) curvatures: κ min = H − H 2 − K κ max = H + H 2 − K Surface shape classes IET 3rd International Conference on Imaging for Crime Detection and Prevention (ICDP-09), Kingston, UK, 3rd Dec 2009
  • 8. Nose feature extraction• Curvature-based landmark detection – Binary convex and concave images filtered using opening by reconstructionBefore After – Nose tip is largest convex region – Nasion is largest concave region above tip IET 3rd International Conference on Imaging for Crime Detection and Prevention (ICDP-09), Kingston, UK, 3rd Dec 2009
  • 9. Nose feature extraction• Geometric nasal proportions – 2 ratios defined: Saddle width Saddle ratio = Ridge length Nose tip width Nose tip ratio = Ridge length – Combined in a 2 element feature vector – Width at centroid more robust IET 3rd International Conference on Imaging for Crime Detection and Prevention (ICDP-09), Kingston, UK, 3rd Dec 2009
  • 10. Nose feature extraction• Nose ridge profile – Defined between nasion and tip • Robust to variations in pose – 3D shape captured by extracting ridge points from range image IET 3rd International Conference on Imaging for Crime Detection and Prevention (ICDP-09), Kingston, UK, 3rd Dec 2009
  • 11. Nose feature extraction• Nose ridge profile represented using Fourier descriptors• Ridge reflected to make closed contour• Coefficients adjusted to make invariant to scale and rotation IET 3rd International Conference on Imaging for Crime Detection and Prevention (ICDP-09), Kingston, UK, 3rd Dec 2009
  • 12. Evaluation of classification performance• Photoface database used – 36 single captures – 4 multiple captures (data-sets A,B,C and D) Dataset C IET 3rd International Conference on Imaging for Crime Detection and Prevention (ICDP-09), Kingston, UK, 3rd Dec 2009
  • 13. Evaluation of classification performance• Training set of 44 images – 36 single images – 2 neutral images from data-sets A, B, C and D• Test set, remaining n-2 images from data- sets A, B, C and D• Euclidean distance used to find closest match in training set for each test image• Random change of correct recognition is 2/44 = 4.54% IET 3rd International Conference on Imaging for Crime Detection and Prevention (ICDP-09), Kingston, UK, 3rd Dec 2009
  • 14. Evaluation of classification performance• Geometric ratios results – Poor rank 1 performance – Rank 10 performance better IET 3rd International Conference on Imaging for Crime Detection and Prevention (ICDP-09), Kingston, UK, 3rd Dec 2009
  • 15. Evaluation of classification performance • Geometric ratios resultsDendrogram of distances between data-sets A (features 1-12), B (features 13-22), C(features 23-28) and D (features 29-34) for the geometric ratios. IET 3rd International Conference on Imaging for Crime Detection and Prevention (ICDP-09), Kingston, UK, 3rd Dec 2009
  • 16. Evaluation of classification performance• Geometric ratios results Input image from data-set D and closest matches from training set IET 3rd International Conference on Imaging for Crime Detection and Prevention (ICDP-09), Kingston, UK, 3rd Dec 2009
  • 17. Evaluation of classification performance• Nose ridge profile – Rank 1 performance much improved – Rank 10 performance slightly worse IET 3rd International Conference on Imaging for Crime Detection and Prevention (ICDP-09), Kingston, UK, 3rd Dec 2009
  • 18. Evaluation of classification performance• Nose ridge profileDendrogram showing the distances between data-sets C (features 1-6) and D(features 11-12), for ridge FD features. IET 3rd International Conference on Imaging for Crime Detection and Prevention (ICDP-09), Kingston, UK, 3rd Dec 2009
  • 19. Evaluation of classification performance• Nose ridge profile Input image from data-set A and closest matches from training set IET 3rd International Conference on Imaging for Crime Detection and Prevention (ICDP-09), Kingston, UK, 3rd Dec 2009
  • 20. Evaluation of classification performance• Cumulative Match Characteristic plot for combined features 1 The Eigennose 0.9 technique applying 0.8 the Eigenface method to the nose Probability of recognition 0.7 0.6 region of the face 0.5 0.4 0.3 Eigennose 0.2 Geometric Ratios 0.1 Ridge FD Combined Geometric Ratios and Ridge FD 0 1 3 5 7 9 11 13 15 17 19 21 23 Rank IET 3rd International Conference on Imaging for Crime Detection and Prevention (ICDP-09), Kingston, UK, 3rd Dec 2009
  • 21. Evaluation of classification performance• Cumulative Match Characteristic plot for combined features The combined 1 Geometric Ratios 0.9 (GR) and Ridge FD 0.8 technique uses the Probability of recognition 0.7 GR to select the 12 0.6 closest faces and 0.5 applies the ridge FD 0.4 to this subset 0.3 Eigennose 0.2 Geometric Ratios 0.1 Ridge FD Combined Geometric Ratios and Ridge FD 0 1 3 5 7 9 11 13 15 17 19 21 23 Rank IET 3rd International Conference on Imaging for Crime Detection and Prevention (ICDP-09), Kingston, UK, 3rd Dec 2009
  • 22. Discussion and conclusions• The nose’s biometric potential is largely unexplored• Curvature provides robust method for identifying landmarks in PS images• Geometric ratios and nose ridge shape both show the nose’s biometric potential• Recognition currently far lower than other biometrics• Evaluation over larger database and in conjunction with other recognition techniques ongoing IET 3rd International Conference on Imaging for Crime Detection and Prevention (ICDP-09), Kingston, UK, 3rd Dec 2009