Efficient Hand Detection on Client-server Recognition System
Victor Chernyshov
Department of Computational Mathematics and Cybernetics, Lomonosov Moscow State University, Moscow, Russia
Keywords:
Hand Biometrics, Client-server System, Continuous Skeletons, Hand Detection, Hand Shape, Finger Knuckle
Print.
Abstract:
In this paper, an efficient method for hand detection based on continuous skeletons approach is presented. It
showcased real-time working speed and high detection accuracy (3-5% both FAR and FRR) on a large dataset
(50 persons, 80 videos, 2322 frames). This makes the method suitable for use as a part of modern hand
biometrics systems including mobile ones. Next, the study shows that continuous skeletons approach can be
used as prior for object and background color models in segmentation methods with supervised learning (e.g.
interactive segmentation with seeds or abounding box). This fact was successfully adopted to the developed
client-server hand recognition system — both thumbnailed colored frame and extracted seeds are sent from
Android application to server where Grabcut segmentation is performed. As a result, more qualitative hand
shape features are extracted which is confirmed by several identification experiments. Finally, it is demon-
strated that hand detection results can be used as a region of interest localization routine in the subsequent
analysis of finger knuckle print. The future research will be devoted to extracting features from dorsal fin-
gers surface and developing multi-modal classifier (hand shape and knuckle print features) for identification
problem.
1 INTRODUCTION
Rapid progress in mobile technologies naturally
causes the development of personal biometrics sys-
tems based on tablets and smartphones. Together
with iris and fingerprints, hand is one of the most
promising biometrics modalities. Characteristics of
modern devices (performance, camera quality, wire-
less communication capabilities) make it possible to
realize various types of architecture of hand authen-
tification/identification application (Franzgrote et al.,
2011):
1. mobile device based (the full cycle of processing
is on board of a mobile device),
2. “truly” client-server architecture (a mobile de-
vice only captures images/videos and sends to a
server),
3. hybrid (the processing stages are shared by a mo-
bile device and a server).
Apparently that schemes 1. and 3. suppose the client-
side of the application to be as fast as possible.
The main objective of this paper is to introduce
fast and reliable method for hand detection, which
can be effectively used in real-time client-server iden-
tification system. The proposed method (Section 3)
is based on continuous skeletons approach compre-
hensively described in the papers (Mestetskiy and Se-
menov, 2008), (Mestetskiy et al., 2011).
Another motive of the study is to show the out-
look of applying skeleton representation for setting
appearance models in segmentation methods with su-
pervised learning (Section 4.1). Accurate segmenta-
tion is an integral part of majority shape features ex-
traction methods.
It is also demonstrated that skeleton hand shape
representation can be used for extraction regions con-
taining finger knuckle prints (Section 4.2). This is the
initial step to start the local texture analysis of dorsal
fingers surface.
Algorithms described in this work are a part of
ongoing project “Mobile Palm Identification Sys-
tem” (MoPIS), earlier introduced in (Chernyshov and
Mestetskiy, 2013).
We remind of the fact that MoPIS system has
a client-server architecture where the client is an
application for Android-based mobile devices, and
the server is implemented with a help of Debian
GNU/Linux distributive, Nginx web-server and pro-
gramming language C++. Android application uses
frames from a high-resolution video camera as input.
461
Chernyshov V..
Efficient Hand Detection on Client-server Recognition System.
DOI: 10.5220/0005315704610468
In Proceedings of the 10th International Conference on Computer Vision Theory and Applications (VISAPP-2015), pages 461-468
ISBN: 978-989-758-090-1
Copyright
c
2015 SCITEPRESS (Science and Technology Publications, Lda.)
Let us highlight the main ideas which have led to
the development of the proposed method.
While processing a frame we usually conduct two
consequent subroutines: hand detection in the frame
(at client-side) and, in the case of a positive outcome,
detailed analysis of this frame with a hand (at server-
side). Due to the network restrictions we can send
only several frames to the server during the identifi-
cation session.
That’s why hand detection procedure should ap-
prove for the further analysis as few images guaran-
teed to be unfit as possible (e.g. we need a low false
positive rate). And at the same time, it should process
a video stream from the camera at wide range of mo-
bile devices in real-time. Thus, the required method
has to meet very strict requirements both to the qual-
ity of recognition and performance.
There have been some research to detect hand us-
ing AdaBoost-based methods with promising results
in accuracy and speed (K¨olsch and Turk, 2004), (Fang
et al., 2007), (Xiao et al., 2010) suitable for use in un-
constrained environments (various kinds of lighting,
diverse background, etc.). But these methods gen-
erally need exhaustive classifier training and a large
dataset including samples with different rotations and
scaling. Also such methods don’t explicitly utilize
any hand geometrics like mutual disposition of fin-
gers or their proportions, making difficult to separate
“bad” hands (e.g. with partially “glued” fingers —
Fig. 3; such sample is ineligible for the shape analy-
sis procedure) from “good” ones. Another approach
is to use skin color based detection (Elgammal et al.,
2009), (Vezhnevets et al., 2003) but it is unreliable be-
cause of sensitivity to lighting conditions and messing
with skin-colored objects. Optical flow methods (So-
bral, 2013) demonstrate good results for stationary
cameras and permanently moving objects, so, cann’t
be directly used in our case without improvements.
The rest of the paper is organized as follows.
Equipment and collected dataset are described in Sec-
tion 2. The hand detection procedure is fully pre-
sented in Section 3. Further server-side image pro-
cessing is given in Section 4. Next, the experiment
results are introduced and discussed in Section 5. Fi-
nally, Section 6 shortly concludes the paper.
2 DATA AND EQUIPMENT
During the research we collected 80 short videos of
hands of 50 different people (1-3 video for each per-
son, the back side of the right hand was captured). All
videos were taken using cameras of mobile devices.
After that they were decomposed into frames (each
5th frame was used), which were saved as graphic
files (*.jpg or *.bmp). As a result, we got 2322 im-
ages.
Important notice: in our work we consider the as-
sistance of participants — the reasonable person’s in-
tention is to be correctly and quickly recognized by
the identification system. Thus, all cases of cheating
(and corresponding videos as well) are excluded from
consideration.
To form a qualitative dataset (as to get adequate
results from using MoPIS) one should follow the rec-
ommendations given below while capturing videos:
1. The videos should be recorded using a mobile
device with a camera matrix resolution at least
1.3 Megapixels (Mp), which produces video files
with a resolution of 640*480 and more and fre-
quency of 15 frames/second or higher. Choos-
ing a low or middle resolution camera gives a lit-
tle chance to extract any promising texture fea-
tures, though shape analysis stays rather effective.
Preference should be given to high-end devices
with hardware autofocus support. The optimal
duration is 3-6 seconds. For video recording one
can utilize an Android application which is a part
of MoPIS, and also similar applications. During
the experiments we mainly used the smartphone
LG G2 with 13Mp front camera (supports auto-
focus and optical stabilization), and recorded HD
video (1920x1080 or 1280x720 resolution and 30
frames/sec frequency) with a help of MoPIS An-
droid application. Also, some data was captured
using Samsung Galaxy Note 10.1 tablet (Fig. 1).
2. The background should be black or dark (homo-
geneity is not necessary) otherwise there may be
problems with binarization by Otsu (Otsu, 1979)
(and therefore with a hand detection in the frame).
This in turn will affect the quality of the further
analysis of the hand. So, we used black homoge-
neous cloth as a background in our experiments.
3. The camera is recommended to be stable, only the
tested hand should move. Modern mobile devices
have good optical stabilization system, so, there
is no need in a tripod or a holder to fix the po-
sition. Nonetheless, some of the videos of our
dataset were made with a help of a tripod (Fig. 1).
4. To increase the variability of frames obtained
from the video the probationer should slowly
move his fingers (bring together and separate
them) in the horizontal plane. One should avoid
sudden movements. To improve the represen-
tativeness of the dataset it’s highly advisable to
record a few videos from each hand in different
lighting conditions.
VISAPP2015-InternationalConferenceonComputerVisionTheoryandApplications
462
5. Experiments should be carried out in good dif-
fused light (artificial or natural), and in its ab-
sence we recommend to turn on the built-in mo-
bile flash.
Figure 1: MoPIS experimental setup. Based on Samsung
Galaxy Note 10.1 (5Mp front camera).
3 CLIENT-SIDE HAND
DETECTION
The first thing we need to do after getting a frame is
perform a rescaling (factor of 1/2, 1/3 or even less;
the majority of experiment was done with 640x360
images) — hand detection procedure is a real-time
routine even at mobile devives.
Next, a binarization is performed and the largest
contours are marked out for further analysis. We have
chosen Otsu binarization (Otsu, 1979) as a primary
method (Fig. 5). The good balance of speed, accu-
racy and versatility was proved by the numerous ex-
periments. Of course, one should follow the recom-
mendations described in the section 2 to get accept-
able quality but really it doesn’t significantly limit the
range of applicability of application in case of “in-
door” usage. Background and lighting restrictions
can be weakened by applying advanced segmenta-
tion routines or utilizing some specific hardware (e.g.
depth cameras like Kinect or Intel RealSense which
are likely to be integrated in future desktop and mo-
bile devices).
In the Fig. 6 you can find the visualized output
of the detection procedure. Though the boundary of
hand (i.e. the result of countour traversing after bina-
rization, green line) is a little bit saw-edged, the whole
detection procedure worked out correctly.
3.1 Skeleton Construction
As it was mentioned in Introduction, the detection
procedure heavily uses continuous skeleton of a bi-
narized image. Skeleton representation of an object
supposed to be a hand is built and afterwards regu-
larized using the same logic as described in (Mestet-
skiy et al., 2011). Both internal and external skeletons
(see Fig. 6 — they are drawn via pink and yellow, blue
and turquoise segments respectively) of hand shape
are used for the further analysis. The hand detec-
tion problem in our case consider low false positive
rate, so, we have to develop an extended check “valid
hand/not valid” that is introduced in the next subsec-
tion.
3.2 Hand Validation
All the checks provided in this section are applied se-
quentially. If we get the false detection result then
the shape is excluded from the following analysis. If
all tests are passed the shape is supposed to be valid
hand.
First of all the internal skeleton is examined to the
depth from the vertices of degree 1. It searches the
branches starting at vertices of degree 1 and ending
at the root (the center of maximal circle with radius
R
max
inscribed in the shape; we also call it center of
hand). Thus, we obtained finger branches candidates.
A branch failed to pass some check is eliminated from
the following processing. If at a certain point we get
less than 5 branches we consider the false detection
result. Similarly, if in the end of validation we have
more than 5 branches.
Radius of maximum inscribed circle is associated
with any point of skeleton. Function R(x) that maps
points of skeleton to radiuses of maximum inscribed
circles is called the radial function. We calculate the
values of the radial function (or its interpolation) in
30 cue points evenly located from the tip to the end
of the finger branch candidate. After it, we apply the
linear mapping X × R(X) → [0,1] × [0, 1]. The ob-
tained function is named normalized branch radial
function. Using train dataset we can easily calcu-
late the lower and upper bounds of this function in
the cue points and than slightly expand this “tube”
EfficientHandDetectiononClient-serverRecognitionSystem
463
called finger boundarycorridor. We consider the gen-
eral boundary corridor for all fingers. To perform the
boundary coridor check one just need to sure that nor-
malized branch radial function is located inside the
boundary corridor (Fig. 2).
Figure 2: Frame 1562.0015. Doted lines are bounds of
finger boundary corridor. Green solid line — normalized
branch radial function of the pointing finger (id = 1).
Figure 3: Frame 1237.0010, cropped. Fingers sticked to-
gether.
The next step is to determine the branch top and
bottom nodes corresponding to the tip and base of a
potential finger. This is done similarly to the method
given in (Mestetskiy et al., 2011). The line connecting
top and bottom nodes is called the axis of the branch.
The total length of branch edges between top and bot-
tom nodes is called length of a potential finger. After
it, we apply several threshold checks (hereinafter, all
the values are obtained from train dataset and all the
distances are normalized to R
max
). We check whether
the bottom node of a finger is at distance from the root
which is greater than the threshold value ε
1
. The same
checking is applied for the top node (threshold ε
2
).
Further, for a given potential finger we delete all
the fingers which bottom nodes are located inside the
bottom node circle of this finger on condition that they
are shorter than the given one.
Next, we come to triples check. The fingers are
arranged in the order of the contour traversal. Triples
of adjacent fingers are examined. For each triple the
middles of the first and the third fingers are connected.
This segment is supposed to intersect the second fin-
ger (the point of intersection should lie within both
segments). Such a heuristics successfully works be-
cause of blob structure of a hand and small axes be-
tween neighboring fingers.
To the present moment we discarded the vast ma-
jority of non-fingers branches. We consider, that the
most distant to the others bottom node belongs to the
thumb. So, we arrange the fingers in the order of the
contour traversal starting from the thumb (id = 0 is
assigned).
Though the validation process above is strict
and reliable, it doesn’t cope with fingers sticked to-
gether (Fig. 3). That’s why a median check was im-
plemented. We consider 4 fingers (without a thumb).
The euclidian distances ρ
i
,i = 1. ..4 from the bottom
node of the fingers to the root are ordered by ascend-
ing, as a reference value we select the second value
d
2
from the beginning. After that, for each finger we
count normalized deviations from the reference value:
η
i
= |ρ
2
− ρ
i
|/R
max
,i ∈ {1,3,4}. It is verified that
these deviations are less than the threshold ε
3
.
4 SERVER-SIDE HAND
ANALYSIS
4.1 Hand Shape Processing
One of the main purposes of a MoPIS client-server
architecture is to remove computationally heavy pro-
cedures (like “clever” Markov Random Fields based
segmentation methods that are usefull for more accu-
rate shape features extraction) from Android applica-
tion to server. At the same time, when the detection
procedure is completed we already know the internal
and external skeletons representation of a hand. And
it can be easily adopted for using as prior for object
and background color models in advanced segmenta-
tion methods with supervised learning (e.g. interac-
tive segmentation with seeds or abounding box).
As seeds we use circles with centers at the ver-
tices of the skeleton graph (Fig. 7) located inside the
bounding box (Fig. 6). For an internal skeleton only
fingers branches and the root circle are used. For an
external skeleton only 4 branches lying between the
fingers axes are considered. Seeds radiuses are the
values of the radial functions in the skeleton vertice
multiplied by a coefficient (we use 0.9 for internal
VISAPP2015-InternationalConferenceonComputerVisionTheoryandApplications
464
skeletons, 0.7 for external). Also we limit the minimal
external seed radius. Finally, we utilized the region
inside bounding box and the seeds as input for graph
cut driven method (Tang et al., 2013) and got really
good segmentation (Fig. 8). The boundary is correct
and smooth — shape is ready to use for features ex-
traction. It should be noted that there is a quantity
of supervised segmentation methods that can be used
jointly with seeds, Grabcut segmentation was selected
because of its accuracy and speed. Experimental re-
sults are discussed in Section 5.2.
4.2 Finger Region Processing
Although hand shape features can be rather informa-
tive in itself (Y¨or¨uk et al., 2006), some authors pro-
pose effective multi-modal hand recognition methods
for example using palmprint and shape (Kumar and
Zhang, 2006), (Kozik and Choras, 2010). Our hand
recognition system MoPIS is designed to work with
two modalities — finger knuckle prints and shape.
Choosing back side of a hand is determined by our ex-
perience while collecting dataset — people find more
comfortable putting palmprint side of a hand down on
a table.
Features extraction from finger knuckle patterns
assumes the presence of high-resolution source im-
ages (Kumar, 2012) or even special capturing de-
vices (Zhang et al., 2010). Moreover, some texture
analysis methods require massive calculations and
cannot be directly used in per-frame processing at
client-side device. On the other hand, sending heavy
full-hand images to server certanly is not the best op-
tion due bandwidth and time limits. A logic alterna-
tive is to perform a fast localization of regions with
finger knuckle patterns at client and then transmit to
server only these regions.
Hand detection procedure 3 gives us all necessary
details to produce accurate and fast fingers localiza-
tion. So, consider the part of a finger branch locating
between the top and bottom finger nodes. The futher
costruction of corresponding finger polygon and its
bounding box is rather trivial (Fig. 9). Since at client-
side hand detection algorithm usually operates with
resized frames we apply a simple linear transform
to polygon and bounding box coordinates to get the
finger region in the source frame (Fig. 10). Further,
extracted region in high-resolution is asynchronously
sent to server. The common size of such regions in
jpeg format is 15-30kB for 1920x1080 source frame
while the frame itself is 250-500kB. Thus, hand de-
tection results can be used as a region of interest lo-
calization routine in the subsequent analysis of finger
knuckle print.
Figure 4: Frame 1562.0015. Source image.
Figure 5: Frame 1562.0015. Otsu binarization.
Figure 6: Frame 1562.0015. After applying detection pro-
cedure. Nonlinear Voronoi sites are yellow and turquoise
“segments”, linear — red and blue segments. Bounding box
of the hand is orange.
Figure 7: Frame 1562.0015. Hand (red) and background
(blue) seeds.
EfficientHandDetectiononClient-serverRecognitionSystem
465
Figure 8: Frame 1562.0015. Grabcut segmentation.
Figure 9: Frame 1562.0015. Finger polygons are green.
Bounding box of the index finger is orange.
5 EXPERIMENTS
All frames from the dataset (see Section 2) were man-
ually marked: either with valid hand or without.
5.1 Detection Testing
The testing scheme for detection was organized as
follows. We made 3 random partitions of our frame
dataset into train and test datasets, containing frames
of 20 and 30 different people respectively. Train
dataset was used to calculate threshold statistics,
test was utilized for quality and speed estimation.
All frames with resolution 1280x720 and 1920x1080
were resized to 640x360. The experiments were run
on a laptop with Intel Core i7 2.4 GHz processor with-
out using multiprocessing routines. According to the
time profiler the most time consuming procedure was
creation of Voronoi diagram — about 60% of com-
putational time. Hand detection results can be found
in Table 1. Each row corresponds to a dataset parti-
tion. The last column contains time per frame (TPS)
values for the whole detection method (summing exe-
cution time of binarization, skeleton construction and
hand validation).
Figure 10: Regions with index fingers of the same person
(id=18) extracted from different hand images. In the top fig-
ure region is obtained from standard photo with resolution
4160x3120, in the middle — from 4160x3120 HDR photo,
in the bottom — from 1920x1080 video frame 1562.0015.
Table 1: Detection results.
# FAR, % FRR, % errors, % TPF, ms
1 3.2 4.1 3.4 40.5
2 2.8 4.6 3.3 40.2
3 3.7 5.2 4.1 40.0
Since proposed detection procedure demonstrates
low error rates (both false acceptance and false rejec-
tion) and real-time processing speed it can be used
in hand biometrics systems, e. g. as a part of client-
side application. The algorithm is robust to image
quality — it works well even with low-resolution im-
ages (e.g. with 320x180). The most false accep-
tance cases are related to hands partially placed in the
frame. Also, there are some difficulties with blurred
frames though we tried to eliminate quick hand and
fingers motions in data acquisition process.
5.2 Segmentation Testing
In Section 4.1 is showed how hand detection re-
sults can be adopted for setting appearance models
in supervised segmentation methods. Grabcut seg-
mentation (Fig. 8) gives an enhancement over Otsu
method (Fig. 5) and it is proven by identification re-
VISAPP2015-InternationalConferenceonComputerVisionTheoryandApplications
466
sults. We used the same hand dataset and testing
scheme as in section above, extracted only shape-
based features and run simple 1NN classifier — utiliz-
ing segmentation produced by Grabcut’s method gave
significantly lower identification error rate then the
same system powered with Otsu segmentation (Ta-
ble 2). The second and the third colums contain iden-
tification error rates for Otsu and Grabcut based sys-
tems correspondively, the last two columns — exe-
cution time of the aforementioned segmentation rou-
tines.
Table 2: Segmentation results.
# Otsu, % Grabcut, % Otsu, ms Grabcut, ms
1 12.3 7.7 0.4 2002
2 13.6 8.1 0.4 1922
3 13.2 8.2 0.4 2107
An average size of colored 640x360 hand im-
ages used in segmentation experiments is 30-50kB
that meets client-server bandwidth limits. This image
quality is sufficient for accurate shape features extrac-
tion — using instead source images with 1280x720 or
1920x1080 resolution didn’t cause any notable iden-
tification results improvements. So, server-side Grab-
cut segmentation combined with client-side seeds
produces solid base for the further hand shape fea-
tures extraction.
6 CONCLUSION
In this paper, a fast and reliable method for hand
detection based on continuous skeletons approach is
presented. It showcased real-time working speed and
high detection accuracy (3-5% both FAR and FRR) on
a large dataset (50 persons, 80 videos, 2322 frames).
This makes the detection method suitable for use as
a part of modern hand biometrics systems including
mobile ones.
Next, the study shows that continuous skele-
tons approach can be used as prior for object and
background color models in segmentation methods
with supervised learning. This fact was successfully
adopted to the developed client-server hand recogni-
tion system — both thumbnailed colored frame and
extracted seeds are sent from Android application to
server where Grabcut segmentation is performed. As
a result, more qualitative hand shape features are ex-
tracted which is confirmed by several identification
experiments.
Finally, it is demonstrated that hand detection re-
sults can be used as a region of interest localization
routine in the subsequent analysis of finger knuckle
print. Applying finger knuckle images for personal
identification in context of client-server hand recog-
nition system is a challenging task for the further re-
search. Also, significant work should be done on
constructing reliable multi-modal (hand shape and
knuckle print features) classifier to improve identifi-
cation results.
ACKNOWLEDGEMENT
The author thanks the Russian Foundation for Basic
Research for the support on this study (grant 14-01-
00716).
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