Nose as a Biometric

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 SmithMachine 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

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


Introduction

• A number of face-based biometrics have been proposed e.g. iris, ear and retina

• The nose is hard to conceal and relatively invariant to expression

• Provides fixation points for face recognition

B. Griaule, “Understanding Biometrics”, Online, 2008.


Introduction

• Noses can be divided into 6 types

• Full 3D nose matching is computationally expensive

• Shape of the ridge important

Nasion


Photometric stereo image capture

• Capture made using PhotoFace system (UWE)– 4 flashguns and 1 200 fps camera– 4 fames captured in ~20 ms

• Albedo image unaffected by lighting

Input images

Surface normals

Bump map


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


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:

KHH −+= 2maxκKHH −−= 2

minκ

Surface shape classes


Nose feature extraction

• Curvature-based landmark detection– Binary convex and concave images filtered using

opening by reconstruction

– Nose tip is largest convex region– Nasion is largest concave region above tip

Before After



• Geometric nasal proportions– 2 ratios defined:

– Combined in a 2 element feature vector

– Width at centroidmore robust

length Ridge widthSaddleratio Saddle =

length Ridge width tipNoseratio tipNose =



• Nose ridge profile– Defined between nasion and tip

• Robust to variations in pose

– 3D shape captured by extractingridge points from range image


Nose feature extraction• Nose ridge profile represented using Fourier

descriptors

• Ridge reflected to make closed contour• Coefficients adjusted to make invariant to scale

and rotationIET 3rd International Conference on Imaging for Crime Detection and

Prevention (ICDP-09), Kingston, UK, 3rd Dec 2009

Evaluation of classification performance

• Photoface database used– 36 single captures– 4 multiple captures (data-sets A,B,C and D)

Dataset C


• 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%



• Geometric ratios results

– Poor rank 1 performance– Rank 10 performance better




Dendrogram 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.




Input image from data-set D and closest matches from training set



• Nose ridge profile

– Rank 1 performance much improved– Rank 10 performance slightly worse




Dendrogram showing the distances between data-sets C (features 1-6) and D (features 11-12), for ridge FD features.




Input image from data-set A and closest matches from training set




• Cumulative Match Characteristic plot for combined features

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The Eigennosetechnique applying the Eigenfacemethod to the nose region of the face



• Cumulative Match Characteristic plot for combined features

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The combined Geometric Ratios (GR) and Ridge FD technique uses the GR to select the 12 closest faces and applies the ridge FD to this subset


• 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

Discussion and conclusions


Nose as a Biometric

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