Hand wrist localization Arabic Sign Language

Hand and wrist localization approach for features
extraction in Arabic Sign Language recognition
Sana Fakhfakh
University of Tunis-El Manar
U2S laboratory
sana.fakhfakh@enis.tn
Yousra Ben Jemaa
University of Tunis-El Manar
U2S laboratory
Yousra.BenJemaa@enis.rnu.tn
Abstract—This paper proposes a new hand detection and wrist
localization method which presents an important step in the hand
gesture recognizing process. The wrist localization step has not
been given much attention and the existing works are limited
and include many conditions.
Our proposed approach was evaluated on a public dataset
whose obtained results underscore its performance. We highlight
through a comparative study with existing work, the superiority
of our approach and the importance of the wrist localization step.
We also propose to benefit from our proposed method which can
be applied in the sign language recognition domain, and more
precisely in the Arabic digit sign language recognition.
Index Terms—Hand segmentation, Wrist localization, Shape
descriptor, Gesture recognition, Arab Sign Language.
I. INTRODUCTION
Human computer interaction(HCI) aims to achieve an easy
communication with computer systems. On top of that, hands
are naturally a means for the user to address his environment;
they are employed spontaneously and are harmonious with the
human nature.
Hand gesture recognition is an important part of
HCI and may considerably ameliorate human-computer
communication. Several hand detection approaches have been
proposed [1] [2]; we can classify them into two categories:
sensor and vision-based approaches. The first category
compels the user to wear a hand device for interaction [3]
[4] like instrument devices, finger markers, etc. Forcefully,
these methods ensure an easy hand detection process and
provide good detection results, but they are unnatural and
uncomfortable for daily applications. The second category
[3] [4]; uses different techniques of computer vision on the
captured images, the skin segmentation is generally the first
step and the obtained skin mask includes only the hand
region. Most of the existent works are limited with clothing
conditions to eliminate the possibility of detection of the
hand and the forearm region.
Many approaches neglected the wrist localization step
although it is a pertinent piece of information in hand
recognition and detection applications such as robotics,
virtual reality, sign language, etc.
Nowadays, it is absolutely necessary to propose a natural
HCI application which does not impose constraints on the
length of the sleeves or the background color. It becomes
crucial to develop an efficient system which ensures a natural
HCI.
This paper proposes a new method for hand extraction
and wrist localization to achieve an automatic recognition
system based on hand gestures without any clothing and
background condition. We focus in this work also on the sign
language domain. As we know, hand gesture recognition is an
important application of sign language interaction; yet, there
is still a complex problem related to the large number of
signs and the choice of the features that characterize each sign.
Many proposed systems for sign language gesture recogni-
tion look at popular sign languages like the American [5], the
French [6], the British [7] and the Chinese [8] sign languages,
but the Arabic sign language [9] is excluded and limited with
different conditions compared to other sign languages.
So, the main contribution of this paper is to propose a
performance method for hand extraction and wrist localization
to achieve an automatic Arabic digit sign language recognition
system without any conditions.
This paper is organized as follows: in Section2, we describe
the proposed approach for hand localization. In Section 3,
we expose our wrist localization process. Section 4 presents
a brief introduction of our proposed hand feature extraction
techniques. In section 5, we discuss the results of our wrist
localization method with a public database and our gesture
recognition system of the Arabic digit sign language. Section
6 concludes this paper.
II. HAND LOCALIZATION
The first challenge to gesture recognition in sign languages
is the localization of the hand in the image.
In this context, many techniques for hand detection have
been developed. The most popular ones use color descriptors
[10] [11] [12] and are based on skin color modeling [13] [14]
[15]. Other approaches use texture analysis [16].
Although, these approaches are not complex and intuitive,
they remain insufficient since they represent only color dis-
tribution. In fact, clothes and hands can have an important
likeness in color to be confused.
To overcome and solve this problem, we propose to apply
Watershed Transform which ensures the division of the image
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into disjointed regions [17] and separates the objects in an
image. In order to solve the over-segmentation problem of the
traditional watershed, an improved algorithm named marker-
controlled watershed [18] [19] is proposed. Its goal is to
detect the presence of homogenous regions in the image by a
set of morphological operations. It is presented in Algorithm 1.
Algorithm 1 Hand localization: Marker-controlled watershed
algorithm used in works [18] and [19]
1. Convert color image into grayscale one.
2. Use gradient magnitude (as segmentation function).
3. Mark the foreground objects.
4. Compute background markers.
5. Compute the Watershed Transform (of the segmentation
function).
6. Isolate out the region of interest from the segmented image
and visualize the result.
Figure 1 illustrates the proposed segmentation process re-
sults.
Fig. 1: Marker-controlled watershed segmentation:
(a) Original image, (b) Grayscale image, (c) Gradient image,
(d) Final image.
III. WRIST LINE LOCALIZATION
Wrist line extraction is very important in order to facilitate
hand feature extraction. It is very necessary to detect only
a hand region of a captured image from a simple camera.
In recognizing gestures process, with the presence of few
forearm region several errors can be presented due to the little
difference in hand area information.
Some works [20] [21] proposed to apply a skin mask con-
taining hand and forearm information. The forearm width is
analyzed by respecting the mask orientation. These methods
are sensitive to variation of gesture and work only when the
hand region is presented.
Other works [22] [23] proposed a wrist localization method
without any clothing constraints by finding the local minimum
of the contour of the skin mask which contains the hand
region. This method has many detection errors such as finding
the finger region instead of the wrist position as illustrated in
Figure 2 (The regions are circled in red.)
Fig. 2: Wrist detection erroneous results: (a) Image rotated,
(b) Obtained result compared with the ground-truth location[23].
To overcome these limitations, we propose a new method
whose steps are presented in Algorithm 2 and whose details
are described in the next subsections.
Algorithm 2 Wrist detection
1. Rotate the hand region in the vertical direction.
2. Bounding box hand region.
3. Divide hand box in 4 equal regions.
4. Find the wrist position only in the 3 lower parts by detecting
the first line characterized by the minimum number of white
pixels (presented in the hand region) and the maximum
number of black pixels(presented in the background region).
5. Remove all pixels below the wrist line detected.
6. Rotate the new hand region in the original direction.
Hand must be presented in the vertical position. In fact, the
main idea in the wrist detection process is to present the hand
in the vertical position and to eliminate the finger region in
the first step. Second, we search for the first minimum width
related to the wrist line position; we start the search from top
to bottom. This proposition reduces the wrist line search fields
and eliminates the possibility of detecting the minimum width
in the finger region (see Figure 3).
Fig. 3: Wrist detection process.
A. Hand adjustment into the vertical direction
The main goal of this step is to put the hand into the vertical
direction. In this context, we opted to extract the straight line
corresponding to a maximum number of aligned points. It can
be related to the elongated finger (see Figure 4(a)) or to the
forearm existing in the hand object (see Figure 4(b)).
775

Line Hough transformation is a regular method used for
line detection in image processing [24]. It can extract easily
the line corresponding to a maximum number of aligned points
[25].
Each line can be represented by two parameters τ and θ
where τ is the perpendicular distance from the origin to the
line, and θ is the angle between this perpendicular line and
the X coordinate axis [26] as shown in Figure 5. We use θ
to define the new orientation of the hand in our adjustment
process.
Fig. 4: Line detection and image rotation in the vertical direction:
(a) Example of line detected in the finger region,
(b) Example of line detected in the forearm region.
Fig. 5: Polar line representation.
B. Proposed wrist line localization process
To keep only the hand, we proceed firstly to bound all
the detected pixels in a rectangular box having the smallest
perimeter and then we estimate the position of the wrist which
represents the extremity of the hand and connects it to the
forearm.
The wrist exists in the lower part of the image and it is
characterized by the minimum width of the forearm. So
it corresponds to the lower horizontal line of the detected
object characterized by the minimum number of white pixels,
presented in the hand region, and the maximum number of
black pixels, presented in the background region.
So to ensure a good wrist line detection in wrist region and
not in finger or forearm region, we propose to find the wrist
after dividing the bounding region in 4 equal parts and search
the wrist only in the lower three parts presented with red color
circle in Figure 6. In fact, the first part is related to the finger
and palm information.
In the next step, we attempt to remove from the image all the
detected pixels below the wrist in order to keep only the hand
as shown in Figure 7.
Fig. 6: Division process: (a) original image,
(b) Rotated image and fixed wrist block search presented with red
circles.
Fig. 7: Wrist localization results: (a) Original image,
(b) Final hand detection after the wrist localization process.
IV. FEATURE EXTRACTION FOR HAND DESCRIPTION
Extracting good features is crucial to gesture recognition.
The features of the image provide a description of its content
such as color, texture and shape. In our context, shape is the
important feature since color and texture remain unchangeable
for the hand for all gestures. As a result, shape has recently
become one of the most promising descriptors that several
approaches have suggested. These descriptors can be classified
in two categories: contour-based shape descriptors and region-
based shape descriptors.
Contour-based shape descriptors include many transformations
such as Fourier Transform (FT) [27], Wavelet Transform (WT)
[28], Curvature Scale Space (CSS) [29], etc. These descriptors
use only boundary information and not inside information
about the shape. Also, these methods cannot be used with
disjointed shapes where boundary information is not clear.
With region-based approaches, shape descriptors use all the
pixel information within a region. These descriptors include
Geometric Moments (GM) [30], Angular Radial Transform
(ART) [31], Zernike Moments (ZM) [32], Generic Fourier
Descriptors (GFD) [33], etc. Although these descriptors are
sensitive to noise and shape variations [33] [32], they provide
satisfactory results.
Consequently, in this paper we have chosen to use region-
based descriptors. This choice is based on their invariants to
776

geometric transformation and their performance to character-
ize hand shape. Also to highlight the superiority of region-
based shape descriptors, we compare them to the commun
contour-based shape descriptors such as the Fourier Descriptor
(FD).
V. EXPERIMENTAL RESULTS
In this section, we present the test protocol and the ex-
perimental results for our proposed method. First, we present
an evaluation step to our proposed wrist detection algorithm
with a public dataset. Second, we apply our hand detection
and wrist localization process to recognize Arabic digit sign
language.
A. Evaluation on a public dataset
1) Database:
Our proposed wrist detection method is evaluated by using a
public hand gesture recognition database (HGR). It includes
Polish Sign Language gestures and American Sign Language.
Other special signs were used as well. The database was pro-
posed to evaluate hand detection and to propose an estimation
system supported by the Polish Ministry of Science and Higher
Education under research grant no. IP2011 023071 from the
Science Budget 2012-2013 1
.
The HGR database is composed of three series: HGR1,
HGR2A and HGR2B. Each series includes three subsequent
data: original RGB images (jpg files), ground truth binary
skin presence masks (bmp files) and hand feature points
location (xml files). In our evaluation process we use the
HGR1 database proposed in the work of [23] to have a faithful
comparison. It contains 899 images related to 25 gestures
presented by 12 individuals with uncontrolled background
and lighting conditions. Figure 8 shows an example from the
HGR1 database
2) Test protocol:
The performances are evaluated with the same conditions as
those [23]. We detect the reference points U’, V’ and W’ for
each image and compare them with the groud-truth points U,V
and W presented in the xml file.
To verify the performance of our wrist detection process, we
calculate the detection error defined in [23] as:
e =
|WW
|
UV
(1)
The wrist is considered detected if eE where E=1.0 is the
maximal detection error. An example is illustrated in Figure
9.
3) Wrist detection process evaluation: comparison with
existent work:
All results are presented in Table I.
We can conclude that our proposed wrist detection process
gives better results, in terms of error detection, compared to
[23]
s approach. This reduction of the error can be explained
by the addition of the hand orientation and the division
step. This offers the possibility to surpass the different error
1The data set is available at http://sun.aei.polsl.pl/?mkawulok/gestures
Fig. 8: Example from HGR1 Database.
Fig. 9: Example illustrated in [23] from a silhouette with the
ground-truth (U, V, W) and detected (U’, V’, W’) points and
possible wrist point areas. The detection errors are
(a) e = 0.13 and (b) e = 0.66.
conditions related to the finger or forearm information as found
in the work of [23].
Figure 10 illustrates the wrist location assured by our ap-
proach.
Fig. 10: Obtained wrist detection results compared with
ground-truth data: work [23] is presented with a red circle and our
approach is presented with a green circle
TABLE I: Error rates results; our approach and [23]’s
approach.
Number of e E Approach [23] Our approach
E = 1.0 131.7 (14.7%) 125 (13.9%)
E = 0.5 323.3(36.0%) 175(19.46%)
The obtained results highlight the performance of our
approach, particularly, the importance of the vertical hand
777

direction and the elimination of the part including finger
information.
B. Application of the proposed approach to the Arabic digit
sign language recognition
The Arabic Sign language is the principal manner of com-
munication between the deaf and the hearing impaired people.
It is not universal and it is very complex with a special rules
and grammars presented with different signs. It can also be
divided into static (digit, alphabet) and dynamic signs (isolated
and continuous words). In this work we are interest only in
the static signs.
1) Proposed database and test protocol:
Until now, Arabic sign language has received little attention
due to its complexity [9]. The most problem is the absence
of standard database [34]. So to evaluate the performance
of our approach, we acquired a new database (our database)
that contains 216 hand images captured by applying different
orientations and different lighting conditions. The database
contains all the Arabic digit sign language (10 Arabic sign
language digit). Figure 11 illustrates some examples from this
database.
Fig. 11: Examples from our database.
This database has been split randomly into two subsets:
training and test. Experiments have been performed on three
random combinations. The recognition phase was executed by
a k-nearest neighbor classifier.
The performances were evaluated in terms of recognition
rate, recall rate and precision rate defined in the following
equations.
Recognition rate =
T otalNumber of gestures correctly identified
T otal number of gestures
(2)
P recision =
Count ofretrieved images relevant to the query image
T otal count of images retrieved
(3)
Recall =
Count of retrieved images relevant to the query image
T otal count of relevant images in the DataBase
(4)
2) Results:
All the results of all the descriptors proposed in Section 4 are
presented in Table II, Figure 12 and Figure 13.
According to these results we can conclude what follows:
• According to Table II Region-based approaches such as
ZM and GFD are more efficient than contour-based ap-
proaches (FT) because they use all the pixel information
within a hand region.
Fig. 12: Recall rate for all descriptors.
Fig. 13: Precision rate for all descriptors.
• ZM descriptors are the most suitable ones in this domain.
They achieved very satisfactory results for complex num-
bers (7, 8, 9). When we examine the seventh, eighth and
ninth digits (see Figure 14), we can notice their similari-
ties due to the absence of a specific finger for each digit
(the index finger for the ninth number, the ring for the
eighth number and the middle for the seventh number).
This gap has been well presented by the Zernike Moments
descriptor. This indicates that the relevant information
(position of the hidden finger) has been defined precisely.
The performance of ZMs is essentially due to the fact that
their principal functions are orthogonal. In consequence,
ZM can characterize an image with no redundancy or
overlap of information between the moments. Hence it
takes into account all the inner details of the shape, that
offer the possibility to present more information over
the unit circle. So the Zernike Moments descriptor has a
strong detection of slight variations in the complex form.
Fig. 14: Similarity between seven, eight and nine digits.
• The GFD descriptors have achieved very satisfactory
performances for digits (1, 2, 3 and 4). When we
examine the characteristics of these digits as well
(Figure 15), we can see that they are represented by
successive fingers. Each of the fingers is elongated,
rounded and convexed outward. These specifications
have been well detected by the polar presentation.
If whenever there are two points inside the convex
shape, it has also a segment connecting these two
points which have already been presented with another
778

angle θ (θ = θ + 2KΠ). The ZM descriptors have an
ambiguity with this situation because they are only able
to describe shape features in a circular direction, but it
was easily done by GFD which presented shape more
precisely in the radial directions. So the Generic Fourier
Descriptor has a strong ability to detect shapes in general.
Fig. 15: Example of one, two, three and four digits presentation.
TABLE II: Recognition rate for all descriptors
Descriptors k = 1 k = 3 k = 5
Zernike Moment 86.77% 92.06% 96.44%
Generic Fourier Transform 77.24% 85.18% 87.29%
Fourier T ransform 69.83% 86.87% 86.27%
In addition, our obtained result presented in Table III proves
the importance of the wrist localization stage to have a faithful
hand feature extraction. Table III illustrates the decrease in
the recognition rate when using ZMD (85.7%) and GFD
(69.73%) without the wrist detection step. These results prove
also the importance of the wrist localization step in the hand
recognition system.
TABLE III: The ZMD and GFD recognition rates without
and with wrist detection step (wds).
Descriptors Without wds With wds
Zernike Moment 85.7% 96.44%
Generic Fourier Transform 69.73% 87.29%
VI. CONCLUSION AND FUTURE WORKS
This paper proposes a new hand detection and wrist local-
ization process for the Arabic digit sign language recognition.
The experimental results underscore our proposed wrist de-
tection method compared with existing works. A comparative
study between different shape descriptors in terms of gesture
recognition rate and precision/recall rates is presented. ZM are
the most suitable ones and they achieved a very satisfactory
results.
As perspectives to this work, we plan to address the wrist
localization step where the hand region is not presented only
in the scene. Also, we intend to propose other classifiers to
improve the performance of our proposed hand and wrist
detection process with an investigation of new hand features
and why not a real-time Arabic sign language application.
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