Jia Yunde

L U Lu

Zhang Xiaoxun

Zhao Youdong

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Ear Recognition Based on Statistical Shape Model 
LU Lu, ZHANG Xiaoxun, ZHAO Youdong and JIA Yunde 
Department of computer science and engineering,  
Beijing Institute of Technology, Beijing 100081, PR China 
{lulu123,jiayunde}@bit.edu.cn
Abstract
Alfred system suggests that ear shape can be used 
as a unique and comparable feature for identity 
recognition. In this paper, we aim to extract ear shape 
features for recognition. Active Shape Models (ASMs) 
is applied to model the shape and local appearance of 
the ear in a statistical manner. In addition, steerable 
features are extracted from the ear image ahead of 
ASMs. Steerable features encode rich discriminant 
information of the local structural texture and provide 
accurate guidance for shape location. Eigenearshape 
is used for final classification. Experiments on an ear 
database show an encouraging performance. Some 
experiments on double ears combined for recognition 
are also carried out, and indicate that the fusion 
outperforms either ear alone. 
1. Introduction 
Using ear in identifying people has been interesting 
in the past few years. There are many methods 
proposed for ear recognition. Burge and Burger [1] 
proposed automating ear biometrics with Voronoi 
diagram of its curve segments. Hurley, Nixon and 
Carter [3] used force field transformations for ear 
recognition. Bhanu [4] proposed an approach of human 
ears recognition in 3D images and presented a new 
local surface descriptor for surface representation for 
ears. Victor, Bowyer and Sarkar [6] made a 
comparison between face and ear recognition using 
principle component analysis. Chang [5] found that 
multi-model recognition using both ear and face results 
in statistically significant important over either 
individual biometric. Moreno et al. [7] presented 
multiple identification method, which combines the 
result from several neural classifier using feature outer 
ear points. 
The two studies from Alfred Iannarelli in 1989 gave 
supporting evidence for ear using as biometrics. The 
first study compared over 10,000 ears drawn from a 
randomly selected sample, and the second study 
examined fraternal and identical twins. The evidence 
from these studies supports the hypothesis that the ear 
contains unique physiological features [1]. Alfred 
Iannarelli has created a 12 measurement system 
“Alfred system” (see Figure 1). The identification 
consists of 12 measurements and the basic information 
about people [2].  
Alfred system suggests that ear shape can be used as 
a unique and comparable feature for identity 
recognition. The objective of this paper is to extract ear 
shape features for recognition. Thus, we use ASMs to 
model the shape and local gray texture of the ear. And 
steerable features are extracted from the ear image 
ahead of ASMs to provide accurate guidance for shape 
location. Eigenshape, which has proved to be 
successful in gait recognition [12], is used for final 
classification. Experiments on an ear database show an 
encouraging performance. Some experiments on 
double ears combined for recognition are also carried 
out, and indicate that the fusion outperforms either 
individual ear. 
2. Ear Shape Model 
The difficulty of application of the “Alfred system” 
is the location of the center point in the system. If the 
first point is not defined accurately, none of the 
measurements are useful [2]. Alfred system suggests 
that ear shape can be used as a unique and comparable 
feature for identity recognition. ASMs, which were 
proposed by Cootes et al. [8], are used to effectively 
model both the shape and local appearance of ear in a 
statistical manner. 
Figure 1 Alfred system 
Proceedings of the First International Conference on Innovative Computing, Information and Control (ICICIC'06)
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2.1. Point Distribution Model 
This model is used to describe a shape and its 
typical appearances. By sampling the coordinates of 
the n labeled points (landmarks) of each ear image and 
concatenating into a vector s, which represents an ear 
shape (see Figure 2): 
ni1;]y,x,...,y,x,y,x[s T1in1ini1i1i0i0i
In order to study the variations of the position of 
each landmark throughout the set of training images, 
all shape must be aligned to each other. This is done by 
changing the pose (scale, rotation, and translation) of 
consecutive shapes until the whole set is properly 
aligned. Then we capture the statistic of shape. 
Principle Component Analysis is applied to the aligned 
shapes: 
Pbss                (1) 
Where s is the mean shape vector, P is the set of 
orthogonal models of shape variation, and b is a vector 
of shape parameters. By varying the elements of b we 
can vary the shape using Equation. 
2.2. Local Appearance Model 
The local appearance models describe the local gray 
features around each landmark. It modeled as the first 
derivative of the sample profiles perpendicular to the 
landmark contour [8]. 
We assumed that local gray models are distributed 
as a multivariate Gaussian. For the thj landmark, we 
can derive the mean profile jg and the sample 
covariance matrix jS from the
thj profile example 
directly. The quality of fitting a feature vector sg at
teat image location s to the thj model is given by 
calculating the Mahalanobis distance from the feature 
vector to the thj model mean. 
)gg(S)gg()g(f js
1
j
t
jssj        (2) 
At the current position s, when searching points, the 
local appearance models find the best candidate in the 
neighborhood of the search point, by minimizing 
)g(f sj [8]. 
3. Ear Shape Model based on 
Multi-orientation and Multi-resolution 
Although ASMs is a powerful statistical tool for 
shape, it can suffer from changes in illumination and 
local minima in optimization and the result depends on 
initialization.  
On one hand, if we ensure that the “best candidate 
point” is included in the profile perpendicular to the 
landmark contour, the profile must be long enough to 
include the target point. On the other hand, the more 
points the profile include, the more consume we cost to 
find the target. The problem can be solved by a 
multi-resolution approach. We must generate a 
pyramid of images with different resolutions and 
obtain the profile statistics for each landmark and each 
pyramidal level. The procedure which starts at the 
highest level is repeated until the lowest (the original 
image) is reached the convergence. 
In ASMs, the local appearance model, which 
represents the local gray model around each landmark, 
can not find the accurate shape but get stuck at the 
local minima. Feng Jiao, Stan Li et al. [9] proposed to 
apply the Gabor to the ASMs. And they proved the 
wavelet features can improve the search accurateness. 
Gabor wavelet feature has achieved great success in 
search shape with the characteristic of 
multi-orientation and multi-scale. However, Gabor 
filters prohibitively time-consuming in image. 
Steerable filter is a kind of oriented filters and it can 
capture the local structure corresponding to any 
orientation on one scale. The magnitude and phase of 
(a) 
(c)
(b)
(d)
Figure 3 the result of shape search introduced 
local steerable magnitude features including four 
orientation: (a) 0 degree,(b) 45 degree,(c) 90 
degree ,(d) 135 degree 
Figure 2 the landmarks of the ear shape, 37 landmarks 
used to represents a shape
Proceedings of the First International Conference on Innovative Computing, Information and Control (ICICIC'06)
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Steerable features encode rich discriminate information 
of the local structural texture of the object as well as 
Gabor filter and provide accurate guidance for the 
search. But steerable filter costs much less computation 
due to its two distinct properties, X-Y separation and 
symmetry or anti-symmetry [10]. Thus, steerable 
features are extracted from the ear image ahead of 
ASMs to improve the performance of ear shape 
location (see Figure 3). 
4. Eigenearshape 
Eigenshape models represent the variability of a set 
of example shapes in a compact and statistically robust 
way [11]. Liang Wang et al. have applied Eigenshape 
to gait recognition [12].In order to obtain the 
Eigenearshape, we re-represent shapes as a set of 
points in a common complex coordinate [12,13]. That 
is, each shape can be described as a 
vector Tn21 ]z,...,z,z[z , where iii y*ixz . For 
two shapes, 1z and 2z , if they are equal through a 
combination of translation, scaling and rotation, we 
may consider they are the same shape [12,13]. 
2k1 z1z ,
j          (3) 
Where k1 translates 2z , and scale and 
rotate 2z , respectively, we may consider they represent 
the same shape [12,13]. 
It is very convenient to center shapes by defining 
the centered vector Tn21 ]v,...,v,v[v zzv ii ,
n
1i i n/zz . The full Procrustes distance 
)v,v(d 21F [12,13] between two configurations can 
be defined as 
2
2
2
1
2
2
*
1
21F
vv
vv
1)v,v(d          (4) 
Which minimizes 
2
2
2
k
1
1
v
v1
v
v            (5) 
Note that the superscript * represents the complex 
conjugation transpose and 1d0 F [12,13]. 
Given a set of n shapes, we can find their mean by 
finding v that minimizes the objective function 
2n
1i
iiki
,
v1vmin
ii
        (6) 
To find v, we compute the following matrix: 
n
1i
i
*
i
*
iiv )vv/()vv(S           (7) 
The mean shape v is the dominant eigenvector of vS ,
the eigenvector that corresponds to the greatest 
eigenvalue of vS [12,13].This vector is used as 
Eigenearshape. 
5. Experiments 
To demonstrate the efficiency of our method, 
extensive experiments are carried out on an ear 
database. Following geometrical normalization, 
histogram equalization is also performed for intensity 
normalization in our experiments. 
5.1. Ear Database 
This database mainly concerns ear recognition with 
slight pose change. Pose change has more influence on 
performance than other factors such as expression and 
illumination [14]. A digital camera (Sony DSC-S85) 
fixed on a tripod is used to capture images. 10 images 
are captured for individual: 5 images for the left ear 
and 5 for the right ear, corresponding to 5 different 
poses. The angle for ear images between two adjacent 
poses is 5 degree. The ED now contains the images of 
56 individuals, namely 560 images totally. The size of 
original image is 640 by 480, while the size of ear 
normalized images is 38 by 60 (see Figure 5).  
(a) (b)
(c) (d)
Figure 4 Gallery and probe set of left and right ears. (a) 
gallery for left ear (b)probe set for left ear (c) gallery for 
right ear (d) probe set for right ear. 
Proceedings of the First International Conference on Innovative Computing, Information and Control (ICICIC'06)
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We divided all ear images into the galleries and the 
probe sets. Two images with the slight pose change are 
included in the gallery for training and reference, and 
other three images are included in the probe set (see 
Figure 4). 
5.2. Ear Recognition 
Eareigenshape is applied in ear recognition. Victor 
et al. have found that the multi-model recognition 
using both ear and face results in statistically 
significant important over either individual biometric 
[5]. We can believe that double ears combined for 
recognition can obtain a better performance than either 
one alone. We experiment on the left ear and the right 
ear respectively. Another experiment was performed to 
investigate the value of a fusion recognition using left 
and right ear. Recognition rate for experiment using 
168-image training sets is 95.1% for the fusion 
recognition versus 93.3% for the left ear and 93.3% for 
the right ear. It indicates that the fusion outperforms 
either ear alone (see Figure 6). 
6. Conclusions 
In conclusion we have showed that ear biometrics can be 
used for identification and for the further development. In 
this paper, we use Active Shape Models to represents the ear 
shape according to Alfred system. Ahead of ASMs, steerable 
features are extracted for improving the performance of 
shape location. Finally, Eigenearshape is applied in ear 
recognition. The experiments on an ear database show an 
encouraging performance. 
7. References 
[1] Mark Burge and Wilhelm Burger. “Ear Biometrics”. 
BIOMETRICS: Personal Identification in a Networked 
Society. 1999, p.273-286. 
[2] Hanna-Kaisa Lammi. “Ear Biometrics”. 
Lappeenranta University of Technology,2004  
[3] David J. Hurley, Mark S. Nixon, John N. Carter. 
“A New Force Field Transform for Ear and Face 
Recognition”, Proceedings of the IEEE International 
Conference of Image Processing, 2000, p.25-28. 
[4] B.Bhanu and H.Chen, “Human Ear Recognition in 
3D”, Workshop on Multimodal user Authentication, 
Dec 2003. 
[5] Kyong Chang, Kevin W. Bowyer, and Sudeep 
Sarkar, Barnabas Victor. “Comparison and 
Combination of Ear and Face Images In 
Appearance-Based Biometrics”. IEEE trans. On PAMI 
Sep. 2003, vol. 25, no. 9. 
[6] B. Victor, K. Bowyer, and S. Sarkar, “An 
evaluation of face and ear biometrica”, IEEE 
International Conference on Pattern Recognition, pp. 
429-432, August 2002. 
[7] Carrira-Perpinan, “Compression neural networks 
for feature extraction: Application to human 
recognition from ear images”, MSC. thesis, Technical 
University of Madrid, Spain, 1995. 
[8] T. F. Cootes, C. J. Taylor, D. H. Cooper and J. 
Graham, Active Shape Models – Their Training and 
Application., Computer Vision and Image 
Understanding, 61(1), 38-59, 1995 
[9] Feng Jiao, Stan Li, Heung-Yeung Shum and Dale 
Schuurmans. Face alignment using statistical models 
and wavelet features. CVPR 2003. 
[10] Xiaoxun Zhang, Yunde Jia. “Symmetrical Null 
Space LDA for Object Recognition”. International 
Conference on Intelligence Computing, 2005 
[11] Baumberg, A. and Hogg, D. An adaptive 
eigenshape model. In Pycock, D. (ed.), 6th British 
Machine Vision Conf., pp. 87–96.BMVA Press.1995 
[12] Liang Wang, Huazhong Ning,Weiming Hu,Tieniu 
Tan. “Gait Recognition based on Procrustes Shape 
Analysis”.IEEE ICIP 2002  
[13] J.Boyd, “Video Phase-Locked Loops in Gait 
Recognition”, in Proc. Of International Conference on 
Computer Vision, 2001 
[14] P.Jonathon Phillips, Hyeonjoon Moon, Syed 
A.Rizvi, and Patrick J.Rauss. “The FERET Evaluation 
Methodology for Face-Recognition Algorithms”. IEEE 
Trans. PAMI, Vol. 22, NO. 10, pp.1090-1104, 2000 
Figure 7 efficiency of the eigenearshape algorithm 
(a)
(b) 
(c) (d)
Figure 5 (a)(c) original images (b)(d)normalized images
Proceedings of the First International Conference on Innovative Computing, Information and Control (ICICIC'06)
0-7695-2616-0/06 $20.00  © 2006


Ear Recognition Based on Statistical Shape Model.

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Ear Recognition Based on Statistical Shape Model.

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