Contactless palmprint recognition systems alleviate the concerns on personal hygiene, acquisition flexibility, etc. Unfortunately, the preprocessing of contactless palmprint image faces several severe challenges, including unconstrained hand placement, complex background, light interference, etc. This paper proposes logical conjunction of triple-perpendiculardirectional translation residual (TPDTR) for the improvement of contactless palmprint image preprocessing. The search of hand valley point is within the borders of hand valley gap detected by TPDTR; therefore, the computational cost is effectively decreased. Furthermore, the anti-interference capacity of region is stronger than that of point and line, so TPDTR improves the accuracy of hand valley point detection. The experimental results confirm the superiorities of TPDTR over the existing methods in computational cost and accuracy. Proceedings of The International Conference on Information Technology: New Generations, USA.

1. Logical Conjunction of Triple-perpendicular-
directional Translation Residual for Contactless
Palmprint Preprocessing
Lu Leng, Gang Liu, Ming Li
Key Laboratory of Nondestructive Test (Ministry of
Education)
Nanchang Hangkong University
Nanchang, P.R.China
leng@nchu.edu.cn, liugang641@gmail.com,
liming@nchu.edu.cn
Muhammad Khurram Khan
Center of Excellence in Information Assurance
King Saud University
Riyadh 11653, Saudi Arabia
mkhurram@ksu.edu.sa
Ali M. Al-Khouri
Emirates Identity Authority
United Arab Emirates
Abstract—Contactless palmprint recognition systems alleviate the
concerns on personal hygiene, acquisition flexibility, etc.
Unfortunately, the preprocessing of contactless palmprint image
faces several severe challenges, including unconstrained hand
placement, complex background, light interference, etc. This
paper proposes logical conjunction of triple-perpendicular-
directional translation residual (TPDTR) for the improvement of
contactless palmprint image preprocessing. The search of hand
valley point is within the borders of hand valley gap detected by
TPDTR; therefore, the computational cost is effectively
decreased. Furthermore, the anti-interference capacity of region
is stronger than that of point and line, so TPDTR improves the
accuracy of hand valley point detection. The experimental results
confirm the superiorities of TPDTR over the existing methods in
computational cost and accuracy.
Keywords-contactless biometrics recognition; logical
conjunction; triple-perpendicular-directional translation residual;
valley point detection; location of region-of-interest
I. INTRODUCTION
Biometric refers to humans’ physiological or behavioral
characteristic, which is more reliable for identity
recognition/verification than possession-based and knowledge-
based methods [1].
Palmprint, as a relatively new biometric, has several
superiorities over other biometrics [2, 3]. The rich and stable
palmprint features can achieve high accuracy performance.
Besides, it can be easily captured by acquisition systems with
low cost. In addition, the user acceptance of palmprint is high.
Due to the several advantages of palmprint, it has been widely
used for identity authentication [4].
Currently, palmprint acquisition systems can be categorized
into contact-type [5] and contactless-type [6]. In contact
acquisition, the background of the acquired palmprint image is
stable and the position of the palm is fixed [7]. Furthermore,
the background is controlled so that it is easy to segment the
hand region and locate the region-of-interest (ROI) [8].
Although contact palmprint recognition systems can
achieve high accuracy performance, some problems occur in
the practical application as follows.
(1) Personal hygiene: Due to the health and personal safety,
it is unhygienic to make the users’ fingerprints or palms contact
the identical sensor or devices for verification, which increases
the risk of infectious diseases.
(2) Lack of acquisition flexibility: The user acceptance is
reduced by the fixing devices that degrade acquisition
flexibility and convenience.
(3) Surface contamination: The surface of contact sensors
in some acquisition systems will get contaminated easily
especially in harsh, dirty, and outdoor environments. The
surface contamination of contact sensors is likely to degrade
the quality of the following acquired palmprint images.
(4) Resistance of customs: Some conservative nations resist
placing their hands on the device that is touched by the users of
the opposite sex.
Thus the research on palmprint recognition system has been
toward contactless-type gradually [9, 10]. Contactless
palmprint recognition systems are significant and a large
number of researchers devote themselves to the preprocessing
of contactless palmprint images.
Preprocessing is the prerequisite of palmprint recognition.
The traditional preprocessing of palmprint includes hand
segmentation, valley detection and ROI location.
Some preprocessing methods for contact palmprint
recognition systems were proposed, such as principal-line-
This work was partially supported by NPST Program by King Saud
University (13-INF943-02), National Natural Science Foundation of China
(61305010, 61262019, 61202112, 61303199), China Postdoctoral Science
Foundation (2013M531554), and Doctoral Starting Foundation of Nanchang
Hangkong University (EA201308058), International Postdoctoral Exchange
Fellowship Program of China.

2. based method [11], feature-point-based method [12],
maximum-inscribed-circle-based method [13], and so on.
Unfortunately, the aforementioned methods cannot be directly
used for the preprocessing of contactless palmprint images due
to the severe challenges as follows.
(1) Position of hand: The appropriate positions of hand
placement are different in different contactless palmprint
systems. However, users can place their hands freely. If the
hand is too far, palmprint details will be lost; while if the hand
is too close, it is probable that some parts of palm are not
captured. Besides distance, the hand can be translated, rotated
and revolved without any restriction. Therefore, it is difficult
to locate ROI of contactless palmprint image.
(2) Interference of complex background: There are many
skin-like regions in the complex background. The complexity
of background in unrestricted environment increases the
difficulty of hand segmentation.
(3) Unstable illumination: The light of the palmprint
acquisition cannot be rigidly controlled in open environment;
therefore, the preprocessing of contactless palmprint image
should cope with the light disturbance, like light intensity,
light color.
Although the development of contactless palmprint
recognition system is still in its infancy, some preprocess
methods of contactless palmprint image have been reported in
the literatures. Competitive hand valley detection (CHVD) was
proposed to locate the ROI of the palm [6]. CHVD, as a
popular ROI location algorithm, was then used in [14] for the
preprocessing of contactless palmprint image. However, the
premise of CHVD is that the hand region can be accurately
segmented from the background; otherwise, the inaccurate
boundary of hand region results in false valley point detection.
In [15], skin color modeling was improved with active shape
model (ASM) [16]. A statistical model of the global shape is
built in ASM to represent a parametric deformable model.
However, ASM relies on the geometry that is sensitive to
interference of complex background. Active appearance model
(AAM) was proposed in unrestricted posture and background
to improve the efficiency, accuracy and robustness [17]. AAM
forms a statistical model of shape and texture together, so the
computational cost is high.
Because of the above challenges of contactless palmprint
preprocess, this paper proposes a fast and accurate processing
algorithm, namely logical conjunction of triple-perpendicular-
directional translation residual (TPDTR), to improve the
preprocessing of contactless palmprint image. The advantages
of the proposed algorithm include:
(1) Reduction of computational cost
The processing capacity in some contactless palmprint
verification systems, e.g. smart card and radio frequency
identification (RFID), is limited, so it is necessary to reduce the
computational cost [18]. The search of hand valley point is
within the borders of hand valley gap between fingers, which
are detected by TPDTR; therefore, the computational cost is
effectively decreased.
(2) Anti-interference capacity
The centroids of four regions of candidate valley points are
computed as the final hand valley points. Since the anti-
interference capacity of region is stronger than that of point and
line, the proposed algorithm improves the accuracy of hand
valley point detection.
The rest of this paper is organized as follows: Section II
presents the proposed the methodology of preprocessing of
contactless palm image. Section III describes the experimental
results. The conclusions are drawn in Section IV.
II. METHODOLOGY
The preprocess of palmprint system consists of three steps.
First, skin-color thresholding method segments hand from the
background. After that, TPDTR is used to detect the borders of
hand valley gap between fingers. Finally, ROI is located
dynamically according to the distance between two selected
valley points. The results of the steps of the proposed
preprocessing algorithm are shown in Fig. 1.
A. Skin-color Thresholding
Skin-color model is used to segment hand from the
background. The RGB color space is not suitable for skin-color
model. In order to overcome illumination disturbance, RGB
color space is converted to YCbCr color space, in which color
and brightness are separated. Besides, human skin colors have
obvious clustering characteristics in YCbCr color space [19].
(a) (b) (c)
(d) (e) (f)
(g) (h) (i)
Figure 1. Results of the steps of the proposed preprocessing algorithm: (a)
Original image; (b) Skin color likelihood image; (c) Binary palm image; (d)
Residual of upward translated image; (e) Residual of left translated image; (f)
Residual of right translated image; (g) Logical conjunction of TPDTR; (h)
Borders of hand valley gap; and (i) Four regions of candidate valley points.

3. Assume the values of the element in Cb and Cr channels are
Cb(i,j) and Cr(i,j), respectively. i and j denote the row and
column of the element, respectively. 1≤i≤h, 1≤j≤w, h and w are
the height and width of palmprint image, respectively. The
human skin color can be modeled as a Gaussian distribution, so
the likelihood of Cb-Cr of the element is:
( ) ( )( ) ( )( )1
, exp 0.5 , ,
T
i j i j i j−⎡ ⎤= − − −
⎣ ⎦
Li c μ σ c μ (1)
where c(i,j)=[Cb(i,j) Cr(i,j)], μ and σ are the mean vector and
covariance matrix of Cb-Cr joint distribution, respectively,
which are determined by a large number of samples. The
original palmprint image is shown in Fig. 1(a). The likelihood
image Li in Fig. 1(b) is thresholded to be the binary palmprint
image L in Fig. 1(c), in which the white region labels the
segmented hand region.
B. Valley Point Detection Based on TPDTR
TPDTR is used to detect the borders of hand valley gap
between fingers, in which the points are checked by three
conditions to search candidate valley point.
Step 1. Translate L along three perpendicular directions (up,
left, right) by a pixel to construct three translated binary palm
images:
( )
( ), 1 ,1
,
0
u
u
i a j i h a j w
i j
otherwise
⎧ + ≤ ≤ − ≤ ≤
= ⎨
⎩
L
L (2)
( )
( ), 1 ,1
,
0
l
l
i j a i h j w a
i j
otherwise
⎧ + ≤ ≤ ≤ ≤ −
= ⎨
⎩
L
L (3)
( )
( ), 1 , 1
,
0
r
r
i j a i h a j w
i j
otherwise
⎧ − ≤ ≤ + ≤ ≤
= ⎨
⎩
L
L (4)
Step 2. The three residual images along three perpendicular
directions, shown in Fig. 1(d)(e)(f), are computed by:
( )
( ) ( )1 , ,
,
0
u
ur
i j i j
i j
otherwise
⎧ >
= ⎨
⎩
L L
L (5)
( )
( ) ( )1 , ,
,
0
l
lr
i j i j
i j
otherwise
⎧ >
= ⎨
⎩
L L
L (6)
( )
( ) ( )1 , ,
,
0
r
rr
i j i j
i j
otherwise
⎧ >
= ⎨
⎩
L L
L (7)
Step 3. L3r, the logical conjunction of TPDTR in Fig. 1(g),
is computed by:
( ) ( ) ( ) ( )3 , = , & , & ,r ur lr rri j i j i j i jL L L L (8)
where & denotes logical conjunction. L3r detects the four hand
valley gaps between five fingers. Four regions, which are larger
than the other regions in L3r, are kept in order to avoid the
interference regions.
Step 4. Translate L3r(i,j) down by b pixels.
( )
( )3 , 1 ,1
,
0
r
er
i b j b i h j w
i j
otherwise
⎧ − + ≤ ≤ ≤ ≤
= ⎨
⎩
L
L (9)
Step 5. Lb, the borders of hand valley gap in Fig. 1(h), is the
logical conjunction of Ler and L.
( ) ( ) ( ), = , & ,b eri j i j i jL L L (10)
Four regions, which are larger than the other regions in Lb,
are kept in order to avoid the interference regions.
Step 6. We use the conditions in [6] to check whether the
points in the four borders of hand valley gap are candidate
valley points. When a point simultaneously satisfies the three
conditions, this point is considered as a candidate valley point.
Condition 1 (Four-point check): Four checking-points are
placed α pixels away from the current point along four
directions (up, down, left, right). According to the prior
knowledge that the position of the hand, we modify Condition
1 with the help of direction information. If the value of the up
point is 0, and the values of the other three points are 1, then
this point satisfies Condition 1.
Condition 2 (Eight-point check): Eight checking-points are
placed α+β pixels away from the current point along eight
directions. If at least one and not more than four values of the
points are 0, while the values of the remaining points are 1,
then this point satisfies Condition 2.
Condition 3 (Sixteen-point check): Sixteen checking-points
are placed α+β+γ pixels away from the current point along
sixteen directions. If there is at least one and not more than
seven values of the points are 0, while the values of the
remaining points are 1, this point satisfies Condition 3.
Unfortunately, in each borders of hand valley gap, more
than one point simultaneously satisfy the above three checking
conditions and are considered as candidate valley points. The
candidate valley points construct four regions shown in Fig.
1(i).
Step 7. (xi
k
, yi
k
)(i=1,2,…,nk) denotes the coordinate of i-th
point in k-th region of candidate valley points. The point of
(xi
k
,yi
k
) is in xi
k
-th row and yi
k
-th column of k-th region. nk
denotes the amount of the points in k-th region. The centroids
of the four regions are considered as the four final valley points
shown in Fig. 2.

4. Figure 2. Four final valley points.
(Xk,Yk) is the centroid of k-th region computed by:
1 1
,
k kn n
k k
i i
i i
k k
k k
x y
X Y
n n
= =
= =
∑ ∑
(11)
Step 8. Four final valley points from left to right are
denoted as P1, P2, P3 and P4, respectively. (Xk,Yk) is the
coordinate of Pk. The following rules determine the left or right
hands, shown in Fig. 3.
Left-hand determination:
X1>X2 & X1>X3 & X1>X4 (12)
Right-hand determination:
X4>X1 & X4>X2 & X4>X3 (13)
C. ROI Location
Select two valley points (P2 and P4 for left hand, P1 and P3
for right hand). Fig. 4 shows how the ROI of the right-hand is
located. The distance between P1 and P3 is m, that is, P1P3=m.
1 2 1 3Q Q PP⊥ , Q1P1=0.2m. The square Q1Q2Q3Q4, with the side
length of m, is the located ROI of the right hand. Similarly, the
ROI of the left-hand can be also located.
III. EXPERIMENTAL RESULTS
Experimental setup is shown in Table I. In order to ensure
the stability of the algorithm, through experiment analysis, a, b
are set to 30 and 5, respectively; while α, β, γ are all set to 5,
respectively.
The palmprint database of Multimedia University [6] is
used for evaluation. In this database, the palmprints of 136
individuals were captured with visible webcams in contactless
environment. The users were from different countries, such as
China, Malaysia, India, Africa, and so on. About ten samples
were captured from each hand for each user. Due to the loss of
some samples, we picked two thousand samples from the
database. The size of the palmprint is 640×480.
Figure 3. Left and right hand determination: (a) Left-hand determination;
and (b) Right-hand determination.
Figure 4. ROI location of right hand.
TABLE I. EXPERIMENTAL SETUP
Setup Parameters
Operating system Windows XP
CPU Intel Pentium 4 (1.60 GHz)
Memory capacity 1024 MB
Software MATLAB 7.1
Since CHVD is a popular preprocessing algorithm of
contactless palmprint image, the experiments compare TPDTR
and CHVD in terms of location accuracy, computation cost and
verification performance.
A. Location Accuracy
Due to the several challenges in contactless palmprint
recognition systems, the segmented hand region is not always
complete, or some non-hand regions are also segmented and
merged into hand region. CHVD relies on the quality of the
hand segmentation; however, the hand segmentation is not
always accurate due to the difference of skin colors and the
complexity of background. On the contrary, TPDTR can
tolerate the error of hand segmentation.

5. (a) (b)
(c) (d) (e)
(f) (g) (h)
Figure 5. Comparison of location accuracy: (a) Original image; (b) Binary
palm image; (c) Edge of CHVD; (d) Valley points of CHVD; (e) ROI of
CHVD; (f) Logical conjunction of TPDTR; (g) Final valley points of TPDTR;
and (h) ROI of TPDTR.
(a)
(b)
(c)
Figure 6. Comparison in other instances: (a) Original contactless palmprint
images; (b) ROIof CHVD; and (c) ROI of TPDTR.
Fig. 5 compares the location accuracy of CHVD and
TPDTR. In Fig. 5(c), besides the real hand contour points, there
are many false contour points caused by complex background,
which leads to the failure of CHVD. In Fig. 5(f), the search of
valley point is within valid regions of hand valley gaps between
fingers, so TPDTR is more robust against false contour.
Therefore, location accuracy of TPDTR is higher than that of
CHVD.
Fig. 6 comparers CHVD and TPDTR in other instances.
B. Computation Cost
In CHVD, the contour is extracted by edge detection. In
edge detection, the pixels in the neighborhood of each pixel are
multiplied by the entries of the mask, and then the products are
summed. Finally, the sums are converted into bits with a
threshold by a judgment. All contour points have to be checked
by the three conditions. Thus the computation cost is large.
On the contrary, in TPDTR, only simple logical operations
and judgments are implemented on binary data. Besides, only
the points in the four borders of hand valley gap are checked by
the three conditions. The amount of the points in the four
borders is less than that of all contour points. Thus the
computation cost is reduced obviously.
Table II compares the computation cost. The execution time
cost of CHVD is the preprocessing from binary palm image
generation to edge detection; while the execution time cost of
TPDTR is the preprocessing from binary palm image
generation to the generation of four regions of candidate valley
points. The execution time of TPDTR is less than that of
CHVD. In CHVD, all contour points need to be checked; while
in TPDTR, only the points in the four borders of hand valley
gap need to be checked. Thus the amount of checking points in
TPDTR is much less.
TABLE II. COMPARISON OF COMPUTATION COST
For one sample CHVD TPDTR
Execution time 1.2885s 0.96583s
Average amount of checking points 3560 550
C. Verification Performance
The PalmCodes [5], which are generated from the ROI
located by CHVD and TPDTR, are compared in term of
verification performance. d' measures how well the genuine
and impostor distributions are separated.
( )
1 2
2 2
1 2
2
d
μ μ
σ σ
−
′ =
+ (14)
where μ1 and μ2 denote the means of the genuine and imposter
distributions, respectively; σ1 and σ2 denote the standard
deviations of the genuine and imposter distributions,
respectively. Large d' implies high separation between genuine
and impostor distributions. The values of the parameters in (14)
are compared in Table III. μ2, σ1 and σ2 of the two algorithms
are similar. μ1 of TPDTR is smaller than that of CHVD, d' of
TPDTR is accordingly larger than that of CHVD.

6. TABLE III. COMPARISON OF VERIFICATION PERFORMANCE
CHVD TPDTR
Accuracy 99.8% 100%
μ1 0.2203 0.2063
μ2 0.4476 0.4476
σ1 0.0036 0.0032
σ2 0.000398 0.000379
d' 5.0839 5.7034
0 0.1 0.2 0.3 0.4 0.5
0
2
4
6
8
10
12
Normalized Hamming distance
Percentage(%)
Genuine(TPDTR)
Imposter(TPDTR)
Genuine(CHVD)
Imposter(CHVD)
Figure 7. Distribution comparison.
10
-6
10
-4
10
-2
10
0
10
2
94
96
98
100
False Accept Rate(%)
FalseRejectRate(%)
TPDTR
CHVD
Figure 8. ROC comparison.
The genuine and imposter distributions of two algorithms
are plotted in Fig. 7. The imposter distributions of two
algorithms are similar; while the genuine distribution of
TPDTR is on the left of that of CHVD, so μ1 of TPDTR is
smaller than that of CHVD, which is coincident with Table III.
Receiver operating characteristic (ROC) curves of two
algorithms are plotted in Fig. 8. The ROC curve of TPDTR is
higher than that of CHVD, so the verification performance of
TPDTR outperforms that of CHVD.
IV. CONCLUSIONS
This paper presents a novel ROI location algorithm of
contactless palmprint, namely TPDTR. The checking of hand
valley point is performed in the borders of hand valley gap that
are detected by TPDTR; therefore, the computational cost is
effectively decreased and the accuracy is improved. The
experimental results confirm the advantages of TPDTR in
location accuracy and computation cost.
ACKNOWLEDGMENT
The authors would like to express their sincere thanks to the
editor and anonymous reviewers for their comments, which
significantly helped to improve this paper. The authors would
also like to thank Multimedia University in Melaka, Malaysia,
for providing us with the palmprint and palmvein databases.
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