This document surveys feature extraction techniques for ear biometrics. It discusses several approaches: Iannarelli's landmark-based measurements; Voronoi diagrams; principal component analysis (PCA); compression networks; force field transforms; iterative closest point (ICP) algorithms; local surface patches (LSP); and acoustic approaches. Each technique is explained and its strengths/weaknesses are noted. The document concludes that while ear biometrics techniques have matured, issues like occlusion, symmetry, and individuality need more research, and that ear biometrics will likely be used more in multi-modal recognition systems.

1. Feature Extraction Techniques for Ear Biometrics : A Survey
Author : Shashank Dhariwal Supervisor : Dr. Sasan Mahmoodi
(sd8g11|sm3@ecs.soton.ac.uk)
Abstract
Identifying people has now become increasingly important due to rise in security concerns in public places. Ear biometrics gives us an opportunity to address this issue as ears are mostly visible unless
occluded partly or completely. Ears are very stable, degrade little with age, and do not change with expressions as the face does. Their position on the side of the head allow easier detection by offering a
predictable background as well as combination with other biometric cues. It. Also, it can be easily captured from a distance without letting the subject know, and hence data can be collected easily. This
study describes the highly efficient ear detection and recognition systems that have been developed over the years.
Ear Structure
Fig. 1. Iannarelli’s System (a) 1-Helix
Rim, 2-Lobule, 3-Antihelix, 4-Concha,
5-Tragus, 6-Antitragus, 7-Crus of
Helix, 8-Triangular Fossa, 9-Incisure
Intertragica. (b) The locations of the
anthropometric measurements used
in the Iannarelli System [1].
Voronoi Diagrams
PCA
Compression Networks
Iannarelli developed an ear
recognition system based
upon 12 measurements
between several landmark
points . He made two
large-scale human ear
identification
studies
consisting of a set of 10000
ears, and the other a set of
ears of identical twins and
triplets.
He concluded that ears in
both the sets were unique
and that the twins and
triplets had similar but not
identical ear structures.
Neural networks are used to process face as
general images for detection and analysis of
facial features like eye distance and chin’s
angle to approximate the position of the ear.
Principal Component Analysis (PCA)
helps in identifying patterns and
determining similarities as well as
differences in data.
Use of compression network (CN) classifier
comprises of two stages.
• CN is trained auto-associatively on the
image to extract some properties. The
vector generated constitutes the input to
a perceptron which is responsible for
identification task.
• The CN is trained as auto-associative
memories which allow coding of the
neural patterns in a small dimensional
subspace by extracting important
features.
The algorithm is as follows:
• Landmark points are recognized
and are used for cropping the
image.
• Cropped image is normalized,
masked to undergo histogram
equalization, and obtain Eigen
faces and Eigen Ears as a result.
Fig. 2: Building a compression network [2].
Force Field Transform
Ear is modelled with an adjacency
graph using curve segments.
Force Field Transform converts the image into a force field by performing invariant linear transform.
The algorithm is as follows:
• Image Acquisition done by taking
a 300 x 500 image of the
subject’s head.
• Localization performed by using
deformable contours on a
Gaussian pyramid representation
of image gradient.
• Computation of edges is done
with the help of Canny operator
and thresholding with hysteresis.
• Edge relaxation is done to form
large curve segments and
remove smaller ones.
• A Voronoi diagram of ear curves
is made and a neighbourhood is
built.
Force fields are used to determine parameters which describe angles and distances between detected energy maxima.
Each pixel is assumed to exert an isotropic force on other pixels proportional to the pixel’s intensity.
The force fields are then mapped onto a potential energy surface with a few potential energy wells and ridges which are
used in matching stage.
Fig. 3: Points used for geometric
normalization of face and ear images
in the PCA-based approach [4].
LSP & ICP
Fig. 5. (a) - Magnitude of Force Field after application on ear image. (b) - Manual initialization of 50 points, formation
of energy lines, obtained energy maxima [3].
Iterative Closest Point
Iterative Closest Point (ICP) is employed for 3D shape recognition matching and use range images as the input data.
2D and 3D data adds to automatic extraction of ear where occlusion is separated and curve estimation is used to identify
the ear pit.
It is followed by initialization of active contour based segmentation to obtain ear outlines.
A combination of 2D colour and 3D depth improves the robustness of the algorithm.
Fig. 4: Neighbourhood Graph built
using Voronoi diagram of ear curves
[1]
Ear detection starts with the
extraction of regions-of-interest
(ROIs) using both the range and
colour
images, followed by
alignment of the reference ear
shape model with ROIs. The local
deformation
driven
by
the
optimization formulation drives the
shape model more close to the ear
helix and the antihelix parts.
Local
Surface
Patch
(LSP)
representation is done by using a
set of descriptors computed at
selected feature points followed by
a two-step ICP algorithm for
matching.
Acoustic approach
The ear by virtue of its special shape
behaves like a filter so that a sound
signal played into the ear is returned
in a modified form.
An ear signature is generated by
probing the ear with a sound signal
which is reflected and picked up by a
small device. The shape of the pinna
and the ear canal determine the
acoustic transfer function which
forms the basis of the acoustic ear
signature.
Linear Discriminant Analysis (LDA)
can be applied to select the most
discriminating components amongst
the subjects.
Fig. 6. Starting from 2D/3D raw data: skin detection, curvature estimation, surface segmentation, region classification,
ear pit detection [5].
Conclusion
• Voronoi Diagrams succeed in avoiding the problem of localizing anatomical points and weakness of basing all feature
measurements on a single point but face the problem of occlusion due to hair and ear rings.
• Compression Networks when combined with facial feature classifiers do not increase the identification rate as the
classifiers are independent.
• Poor invariance remains a major issue for PCA. Another study shows that there is no significant difference between
ear and face recognition, and that the difference in performance of the two studies I due to less control over lighting
conditions and occlusion.
• Force Field Transform offers robustness, reliability, invariance and excellent noise tolerance. However, it faces a
problem when dealing with computation of higher- dimensional data.
• 3D Shape recognition shows that three dimensions can handle the problems of occlusion in a better way and give
excellent results.
• Acoustic recognition demonstrates a completely different technique as it takes advantage of the ear’s acoustic
features. It has an added advantage that it is low cost and publically acceptable.
The techniques developed have been very efficient and have matured to a certain level; the only glitch being their
evaluation done under controlled environment. Issues such as occlusion, symmetry and individuality of ear as well as
validity of ear prints need to be taken care of. With ear biometrics still evolving, we expect its greater utilization in the
upcoming uni-modal and multi-modal recognition systems.
Fig. 7. Top part of figure shows the
ear detection module and bottom
shows the ear recognition module
using the ear helix/ antihelix and
LSP representations [6].
References
Fig. 8. An acoustic probe wave is
sent into the ear canal while a
microphone receives the response
[7].
[1] M. Burge and W. Burger, Ear Biometrics - In A. Jain, R. Bolle and S. Pankanti(Eds.), Biometrics: Personal
Identification in a Networked Society, Kluwer Academic, (1998).
[2] B. Moreno, A. Sanchez, J.F. Velez, On the Use of Outer Ear Images for Personal Identification in Security
Applications, Proc. of IEEE Conf. On Security Technology, pp. 469-476, 1999.
[3] D.J. Hurley , M.S. Nixon, J.N. Carter, Force Field Energy Functionals for Image Feature Extraction,, Image
and Vision Computing Journal, vol. 20, no. 5-6, pp. 311-318, 2002.
[4] K. Chang, K. Bowyer, S. Sarkar and B. Victor, Comparison and Combination of Ear and Face Images in
Appearance-Based Biometrics, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.25, No 9, (2003)
[5] P. Yan, K. W. Bowyer, ICP-based approaches for 3D ear recognition, Proc. of SPIE Biometric Technology for
Human Identification, 282291, 2005.
[6] H. Chen, B. Bhanu, Human Ear Recognition in 3D, IEEE Trans. On Pattern Analysis and Machine Intelligence, vol.
29, no. 4, 718-737, 2007.
[7] A.H.M. Akkermans, T.A.M. Kevenaar and D.W.E. Schobben, Acoustic Ear Recognition for Person Identification,
Proc Auto ID, Philips Research, 2005.