Biometrics Recognition based on Image Local Features Ordinal Encoding

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Biometrics Recognition based on Image Local
Features Ordinal Encoding
Simina Emerich
Department of Communications,
Technical University of Cluj-Napoca,
28 Memorandumului, Cluj-Napoca, 400114, Romania
Bogdan Belean
National Institute for Research and Development of Isotopic
and Molecular Technologies, CETATEA Research Centre,
67-103 Str. Donat, Cluj-Napoca, 400293, Romania
Abstract—In the present informational era, with the continue
extension of embedded computing systems, the demand of faster
and robust image descriptors is an important issue. However,
image representation and recognition is an open problem. The
aim of the paper is to embrace ordinal measurements for image
analysis and to apply the concept for a real problem, such as
biometric identification. Biometrics provides a robust solution
for the identity management process and is increasingly more
present in our life. To explore the textural discriminative
information of images, the paper proposes a new feature
extraction technique, namely, Image Local Features Ordinal
Encoding.
Keywords—Biometrics; image local features; ordinal
measurements; iris; dorsal hand veins
I. INTRODUCTION
The recent advancements achieved in computer vision,
together with sensors evolution, play a key role in the
development of real-world applications. A privileged area of
applications where ordinal analysis and encoding procedures
have been found to be relevant is pattern recognition.
Biometric recognition is defined as automatic person
identification based on vectors derived from biological
characteristics. Ordinal measurements were employed by
several biometric systems especially for signal or image
feature extraction purposes, mainly because of their
computational properties. A brief description of the main
pattern recognition techniques based on ordinal comparisons
with notable influence on image processing is further
presented.
In 1990, He and Wang proposed the first method for image
encoding based on the intensity value of the pixels from a
local neighborhood, namely Texture Unit Number (TUN) [1].
In [2] one of the most powerful texture descriptors Local
Binary Patterns (LBP) were introduced. The LBP operator
compares each image pixel with its 8 neighbor pixels: if the
neighbor pixel is greater or equal then the central one, a binary
1 is resulted, otherwise a binary 0 is used. Finally, a 256 values
histogram is used to collect the occurrences of local patterns.
To reduce the dimensionality Ojala et al. [3] observed that if
only the ’uniform’ patterns (where the maximum number of
bit-wise changes is 2) are retained, the discrimination
performance remains similar. A significant advantage of this
technique is that avoids the need of using time or frequency
normalization. Lately many variations were proposed in
literature, starting from this methodology, LBP and its variants
have been successfully used for image recognition tasks
including biometrics. For example, the use of several patch-
based image descriptors: Local Binary Pattern, Local Phase
Quantization and Differential Excitation, has been
investigated in [4] for iris recognition.
Another approach, named Local Line Binary Pattern
(LLBP) determines a line binary code along with horizontal
respectively vertical direction and its magnitude, and
characterizes the change in image intensity such as edges and
corners [5]. It was applied especially in biometric recognition
systems based on hand veins structure from finger [6], palm
[7] or the dorsal part [8].
Wang et al. proposed in 2011 a highly discriminative
method called Local Intensity Order Pattern (LIOP) that uses
the advantages offered by ordinal measurements to extract the
image descriptors [9]. The input image is partitioned into
square patches with odd length and then each local patch is
divided into sub-regions with the same intensity. Next, a Local
Intensity Order Pattern of each point is computed, based on
the relationships among the intensities of its N neighboring
points. For each vector, a mapping is done by sorting the
elements of the vector in an increasing order and assigning an
integer value from 1 to N!, since there are N! possible
permutations. This approach explores the fact that the relative
order of pixel intensities remains unchanged when the
intensity changes are monotonic [10] and has been
successfully applied in [11] to extract the representative
information from iris texture.
Other ordinal measurements based methods proposed in
literature to solve various problems such as image recognition,
tracking or classification are: Ordinal and Spatial information
of Regional Invariants (OSRI) [12], Multisupport Region
Order-based Gradient Histogram (MROGH), Multisupport
Region Rotation and Intensity monotonic invariant Descriptor
(MRRID), etc. [13]. A very comprehensive study of image
ordinal descriptors was recently published by Fan and Wang
in [14].
The rest of the article is organized as follows. In Section II
we present the proposed technique, namely Image Local
Features Ordinal Encoding (ILFOE). It will be integrated, in
Section III, in two biometric systems, based on iris
respectively dorsal hand veins. Finally, Section IV concludes
the paper.

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II. IMAGE LOCAL FEATURES ORDINAL ENCODING
It is well-known that a pattern recognition algorithm needs
to solve the following three problems: what to measure, how
to measure and how to interpret the results.
The human brain capabilities of visual pattern recognition
remain poorly understood. Some recent studies state that the
computational models disposed by the visual cortex are based
on qualitatively comparing rather than quantitative
information. According to this idea, a new method applicable
for image recognition and based on ordinal measurements, is
further proposed. Qualitative comparisons, associated to the
relative ordering of extracted characteristics, are defined as
ordinal measurements.
Usually, an automatic image classification approach
contains three main modules: firstly, relevant features are
extracted from images, the features are then encoded into
descriptors and finally, the classification is achieved by
disposing the image descriptors into a machine learning
algorithm. The encoding process affects the system efficiency
(speed and accuracy).
Of late years, different descriptors have been designed to
improve the performance of standard histogram encoding
procedure. The Bag-of-Features encoding model has been
extensively explored for image recognition. The most known
Bag-of-Features strategies are: Voting Based Methods (Hard
Voting and Soft Voting), Fisher Kernel, Sparse Coding, Local
tangent Coding, Super Vector Coding, Salient Coding, etc.
[15], [16].
Further we focus on the first two step of this pipeline. The
framework of the proposed ordinal image encoding algorithms
is presented in Fig. 1.
The local features could be extracted from image filter
responses or from image patches. The selection of the
optimum filter banks is application dependent. The use of
image patches is considered to be faster and less complex than
using image jets [17].
The designed method, namely Image Local Features
Ordinal Encoding (ILFOE), consist of representing images
through the differences between patch based local features.
The intensity pixels values will be explored by different
local processing algorithms on a squared or circular
neighborhood. The employed techniques must satisfy several
requirements: should be highly informative and should capture
textural variation. The number of features resulted from each
patch is preferable be fixed and small.
Fig. 1. Image ordinal encoding.
Fig. 2. ILFOE Algorithm.
The ordinal relation between pairs of features extracted
from succesive patches is further investigated. The algorithm
implies qualitative feature comparisions such as greater than,
less than and equal. Fig. 2 summarizes the proposed ILFOE
algorithm.
For each comparison, a ternary code (+, 0, -) is established,
as follows: if the difference between considered features is
greater than a threshold value t, then “+” is assigned to it. A
difference lesser than -t is encoded with “–” and a difference
value in the range of width t around zero is quantized to “0”.
 
 
  (1)
Where  











are local features, A and B are
successive neighborhoods and t is a predefined threshold.
The signs of the differences between adjacent
neighborhoods are encoded into symbols, resulting in n3
distinct values, where n is the number of the considered local
features. The proposed method converts the input image into
an encoded stream of discrete numerical symbols, resulted
from ordinal comparisons, as shown in Fig. 3. It is expected
that the compactly extracted vectors will facilitate the
matching process. The new approach is very flexible and
could be adapted so that region patterns to be constructed
dependent upon the image classification task.
For unidimensional signals, a similar procedure, namely
TESPAR DZ has been proposed in [18] and successfully
employed especially for speech analysis.
Fig. 3. Discrete numerical symbols resulted from ordinal comparisons.
Input
Image
Ordinal
Encoding
Resulted
Histogram
Local
Features

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III. DESIGNING A GENERAL FRAMEWORK FOR BIOMETRIC
RECOGNITION BASED ON ORDINAL MEASURES
The paper intent is to argue that ordinal image
representation provides an appropriate solution for efficient
biometric authentication. The presented method will be
integrated in two recognition systems, based on iris
respectively dorsal hand veins. The processing flow used to
implement the biometric systems is presented in Fig. 4.
Fig. 4. Biometric system processing flow.
A. Unimodal Biometrics Systems based on Iris and Dorsal
Hand Veins
In present, the identity management solutions based on
both iris and hand veins recognition have received a
considerable attention. Compared with others physiological
traits, iris and dorsal hand veins are less susceptible to damage
or forgery and remains unchanged for a long period of time.
To evaluate the iris biometric system, experiments were
made on two publicly available databases: UPOL and
CASIA_V1. The first one includes high resolution and texture
rich color iris images, captured under visible lighting from 64
persons (3 for each eye) [19]. The second database contains
greyscale images, collected from Asian persons [20]. The iris
region is often covered by eyelids and eyelashes (the iris
content is less than 67%, for 11% of images [21]). Different
experiments have been conducted by considering 93 users and
5 different images for each individual, taken from the same
eye.
The inner and outer boundaries of the iris were delimited
during segmentation process by the help of the circular Hough
transform. The region of interest (ROI) was then unwrapped
into polar coordinates and used for the feature extraction step.
Since the upper and bottom of the iris, are often occluded by
eyelashes or eyelids, we investigate our method on the side
parts only. The half iris area (8 blocks) was selected between
315° and 45° for the right side and between 135° and 225° for
the left side. After segmentation step, 8 blocks of same
dimension are selected from the unwrapped rectangular iris
image according to Fig. 5.
The biometric system based on dorsal hand veins has been
designed by using the NCUT Part A database. For
experimental setup, 1020 near infrared gray images collected
from the left hands of 102 individuals (10 samples / user) has
been considered [22]. After region of interest selection, the
following techniques has been employed for image
enhancement: an adaptive histogram equalization with
Rayleigh distribution, followed by a median filtering and an
anisotropic diffusion. The resulted image has been further
divided into 9 equal blocks as shown in Fig. 6.
Fig. 5. Half iris area selection (8 blocks).
Fig. 6. A. Original image; b. Selected and enhanced ROI.
The ILFOE technique is applied independently on each
block to encode the pattern information. Different local
features were estimated, from a k-by-k neighborhood, around
each reference pixel:
 The range value (maximum value − minimum value) of
the neighborhood.
 The median value.
 A pixelwise adaptive Wiener value based on local mean
and variance.
 The standard deviation.
 The entropy value.
 The local binary pattern.
 The rotation invariant local binary pattern, etc.
The resulted image is of the same size as the input one for
all above mentioned techniques. Different experiments were
conducted to select an appropriate combination of individual
features for biometric recognition. Best recognition rates were
achieved by incorporating local range with median and
pixelwise adaptive Wiener values. A symmetric and
rectangular 9x9 neighborhood centered at each pixel, yield the
most accurate prediction in case of iris based system, while for
hand veins system a 5 x 5 neighborhood seems to be the most
appropriate.
For gray images, the range, median and Wiener values,
extracted from image patches, were further converted into a
vector based representation by means of the proposed ordinal
encoding procedure. Another modality, adequate to color
images, was designed by encoding the same local feature,
extracted from R, G, B color channels separately.
On these particular situations, n=3 thus 33= 27 symbols are
generated for each sub-image. The final vector consists in
N*27 features, where N is the number of considered blocks: 8
for iris images and 9 for dorsal hand veins images. It is
independently of input image size and was obtained by simply
concatenating the local histograms. Since the proposed
technique is applied on sub-images (blocks) separately, the
possible artifacts (segmentation errors, occlusions, etc.) will
influence only the corresponding local vector. The recognition
rates are listed in Table I.
Feature
Extraction
from Patches
Database
Encoding
(ILFOE)
Yes / No
Matching
and
Decision
Iris/ Hand
Image
Preprocessing

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TABLE I. RECOGNITION RATES FOR THE UNIMODAL BIOMETRIC
SYSTEMS
System
Image
Type
Database
Classes
Local
Features
Feature
Vector
Length
Train
/Test
Ratio
Accuracy
%
IRIS
Gray
Images
CASIA_V1
93
Range
+
Median
+
Wiener
8*27
3/2
97.84
UPOL
64
8*27
2/1
96.87
Color
Images
UPOL
64
Range
8*27
2/1
95.31
1/2
90.62
Median
8*27
2/1
100
1/2
99.21
Wiener
8*27
2/1
100
1/2
100
HAND
VEIN
Gray
Images
NCUT
102
Range
+
Median
+
Wiener
9*27
6/4
94.85
7/3
96.40
The Support Vector Machine, RBF kernel, has been used
for the recognition task since it has been successfully applied
in many studies for object classification [23].
B. Bimodal Biometric Systems based on Feature and Score
Level Fusions
A single biometric trait does not satisfy all the
requirements (e.g. accuracy, permanence, circumvention, etc.)
especially when it comes to large-scale authentication systems
[24].
Therefore, a bimodal recognition system based on iris and
dorsal hand veins has been also designed, by considering 93
virtual users. Each subject from CASIA_V1 iris database has
been combined with a subject belonging to NCUT vein
database. The use of virtual subjects is a common and
accepted procedure in biometrics.
The fusion has been made at the feature level and
matching-score level. The second strategy combines the
matching scores of each unimodal system, in order to arrive at
a final decision about the users’ identity. The scores provided
by individual matchers are incorporated by the product rule.
The recognition rates are listed in Table II.
The proposed new technique is considered to be suitable
for portable applications, especially due to the
computationally low costs.
Table III presents comparative summary of several prior
approaches presented in the literature for iris respectively
dorsal hand vein authentication. For the selected systems,
different ordinal based methods have been employed for
image analysis.
TABLE II. RECOGNITION RATES FOR THE BIMODAL BIOMETRIC SYSTEM
System
Database
Users
Train
/Test
ratio
Feature
Vector
Length
Methods
Accuracy
(%)
Unimodal
IRIS
CASIA_V1
93
3/2
8*27
Range
+ Median
+ Wiener)
97.84
HAND
VEIN
NCUT
93
3/2
9*27
Range
+ Median
+ Wiener
86.02
Bimodal
IRIS
&
HAND
VEIN
CASIA_V1
+
NCUT
93
3/2
8*27
+
9*27
Feature
Level
Fusion
99.46
8*27
&
9*27
Score
Level
Fusion
98.38
TABLE III. BIOMETRIC SYSTEMS BASED ON ORDINAL MEASURES
System
Database
Methods
Accuracy
(%)
IRIS [25]
CASIA
LBP
+ combined Learning Vector
Quantization Classifier
99.87
IRIS [11]
CASIA
Local Intensity Order Pattern (LIOP)
96.77
UPOL
100
HAND VEIN
[26]
NCUT
Partition Local Binary Pattern (PLBP)
90.88
HAND VEIN
[27]
NCUT
Local Binary Pattern
+ geometry features (crossing, end-
points)
96.67
HAND VEIN
[8]
NCUT
Riesz Wavelet
+ Local Line Binary Patern
+ Statistical Moments
87.9
IV. CONCLUSIONS
One fundamental issue in pattern recognition consists in
finding a convenient method for image to symbol
transformation. The present paper proposes a novel technique
resulted by integrating local image features into an ordinal
measurement based encoding method.
The obtained results indicate that ILFOF method
constitutes a promising solution for image features extraction
that could be easily adapted to different matching or
recognition tasks. The proposed technique is fast to compute,
has a low memory cost and can successfully address real
world applications such as biometrics.
Low computational complexity, large tolerance to
illumination variations and high degree of accuracy are
particular benefits provided by the designed biometric system.
Also, fixed length feature vectors are desirable as inputs for
the classification module.
To validate the effectiveness and the applicability of the
proposed encoding procedure, future work will examine the
ILFOF potential on other application area, such as medical
imaging. Future research will also explore other methods for
image local features extraction.
ACKNOWLEDGMENT
This work was supported by a grant of the Romanian
National Authority for Scientific Research and Innovation,
CNCS – UEFISCDI, project number PN-II-RU-TE-2014-4-
2080.

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