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International Journal of Advanced Network, Monitoring and Controls          Volume 04, No.01, 2019 
72 
Research on Harris Corner Detection Method in 
Palmprint Recognition System 
Wu Hejing 
East University of Heilongjiang 
150086 
e-mail: 499917928@qq.com 
 
 
Abstract—Palmprint location is the premise of feature 
space extraction and feature recognition,the speed and 
accuracy of palmprint location directly affect the speed 
and accuracy of palmprint recognition system, and the 
extraction of contour feature points is the key of 
palmprint location. The contour of palmprint is 
extracted by gray morphological gradient;then, based on 
the analysis of palmprint appearance characteristics, 
Harris corner is used to extract the key feature points of 
the image, and the reference coordinate system is 
established according to the key points to realize the 
location and segmentation of palmprint. 
Keywords-Palmprint Recognition System; 
Palmprintlocation; Harris Corner 
I. EXTRACTION OF CONTOUR FEATURE POINTS BY 
IMPROVED HARRISCORNER DETECTION METHOD. 
 
Figure 1. Palmprint corner detection 
Harris corner detection algorithm is a common 
corner extraction algorithm at present, but it can not get 
ideal effect when we use it directly to extract the 
contour feature points defined by us. As shown in 
Figure 1, this paper improves Harris corner detection 
algorithm purposefully, thus realizing the extraction of 
palmprint contour feature points. 
II. HARRIS CORNER DETECTION 
The predecessor of Harris algorithm is Morave 
algorithm. Morave's corner detection formula is: 
1) In formula E, the brightness change occurs 
when a small window (u, v) is moved at a point (x, y). 
w (x, y) is a Gaussian smoothing factor.The essence of 
formula (2.11) is the autocorrelation of 
two-dimensional signals. The above formula is 
expanded by Taylor series: 
2) Formula: Represents the horizontal and vertical 
derivatives of the point in the image respectively. 
Ignore the higher order terms and write them into 
quadratic form: 
 
3) Formula: 
After M similar diagonalization, the results are as 
follows: 
The eigenvalue of matrix M is obtained. Because 
matrix M has rotation invariance and is proportional to 
DOI: 10.21307/ijanmc-2019-037
International Journal of Advanced Network, Monitoring and Controls          Volume 04, No.01, 2019 
73 
the curvature of gray scale of pixel points, if the 
minimum eigenvalue is greater than a given threshold, 
it is determined as a corner point. 
Harris algorithm needs to determine threshold, 
variance of Gauss function and constant variable K. 
When the image size is, the window size of derivative 
is, and the window size of Gauss filter is, the 
complexity of the operator is, and the algorithm is 
slow. 
III. IMPROVEMENTS OF HARRIS CORNER 
DETECTION ALGORITHMS 
In Harris algorithm, the eigenvalues of matrix M are 
closely related to the first derivatives of pixel points in 
X and Y direIn the edge region, there is only a large 
change in the horizontal or vertical direction, that is, 
only one of them is largections; In flat areas, the 
changes in horizontal and vertical directions are the 
smallest, that is, they are small;Corner area in the 
horizontal and vertical direction of the changes are 
larger points, that is, larger. According to this feature, 
the corner detection algorithm is improved. 
The feature points of palmprint contour extracted by 
us are located in the convex and concave areas between 
fingers on Palmprint contour. They are composed of 
some flat areas and points that vary in a certain range 
of slope areas. 
In the image region, the difference between the 
third line and the first line is close to the derivative in 
the Y direction.The difference between the third 
column and the first column is close to the derivative in 
the X direction. Firstly, two templates DX and Dy are 
designed. For any image region Z, the derivatives of 
center point Z5 in X and Y directions can be defined, 
respectively: 
 
 
 
 
 
Figure 2. The image region 
The input image is the palm edge image after 
thinning. According to the principle of image graphics, 
it is a closed curve composed of continuous single 
pixel points. Its gray value is only 0 and 255. It can be 
defined that the black edge point is response point 1 
and the white background is non-response point 0, then 
when there is no response point in Z region (that is, all 
white back) ==0; 
For the refined contour line, the corner function 
value C is calculated. In the case of C > 0, the 
candidate corner points are obtained. As shown in 
Figure 2 (b), the X coordinate values of the candidate 
corner points are sorted, the array of corner points is 
determined, and then the maximum y value of each 
corner point group is obtained as the corner points we 
want, as shown in Figure 2 (c). In order to further 
verify the feasibility of this algorithm, we have 
searched for some representative images to test the 
performance of corner detection algorithm, such as 
pentagonal star edges and some building blocks edges 
as shown in Fig. 3 (a), and detected the points that 
satisfy the condition C > 0. The results are shown in 
Fig. 3 (b). When C > 0 (i.e., the detected feature 
points). 
 
 
 
 
 
 
International Journal of Advanced Network, Monitoring and Controls          Volume 04, No.01, 2019 
74 
  
 
Figure 3. Extraction of palmprint contour feature points 
IV. EXTRACTION PROCESS OF PALMPRINT CONTOUR 
FEATURE POINTS 
Consistent with our previous deduction, it contains 
not only the points in the top corner areas, but also the 
points on the edge of the inclined angle, which is up 
and down the horizontal line (as shown in the shadows 
in 
 
 
Figure 4. Palmprint contour feature points 
 
Figure 5. Declination range 
The purpose of localization and normalization of 
palmprint image is to extract appropriate reference 
points from Palmprint and establish reference 
coordinate system to reduce the influence of non-linear 
factors such as rotation, translation and distortion 
introduced in the sampling process and improve the 
robustness of matching recognition algorithm. Good 
positioning results can not only provide reference 
frame for other palmprint features, but also provide 
benchmark for palmprint matching and feature 
matching. At the same time, it can segment the central 
area of palmprint, reduce unnecessary noise 
interference, reduce the complexity of subsequent 
matching algorithm, achieve azimuth-independent 
matching, ensure the accuracy and effectiveness of the 
recognition system, and in palmprint identification 
system. It has very important significance. 
 
 
International Journal of Advanced Network, Monitoring and Controls          Volume 04, No.01, 2019 
75 
 
 
Figure 6. Palmprint location and feature space extraction 
After extracting the corner points, we set up the 
coordinate system according to the following steps: 
make the line L1 = K1K2, draw an axis perpendicular 
to the line L1 from K3, and we set it as the Y axis; 
make a line L2 parallel to the line L1 and the crossing 
point K4, obviously the line L2 is perpendicular to the 
Y axis, which can be defined as the X axis, and the 
intersection point of the X axis and the Y axis as the O 
point, as shown in Fig.6 (a).The palmprint image is 
rotated counter-clockwise along the O-point, as shown 
in Fig.6(b), then the central area of palmprint is 
extracted and normalized as the Palmprint Feature 
space, as shown in Fig.6(c). 
Under the above acquisition conditions, a palmprint 
test image database is established, which is composed 
of 80 images with a size of 40*2 (40 persons, each 
person collects 2 images).The positioning accuracy is 
shown in Table 1. The experimental results show that 
the desired feature points can also be found for the 
images with unsatisfactory collection effect. Among 
them, 79 images can be located accurately according to 
the above algorithm, so the accuracy rate is 98.75%. 
TABLE I. LOCATION RESULTS 
Total image count Correct Location Number Error Location Number Location accuracy 
80 79 1 98.75％ 
 
According to the analysis of palmprint images, the 
main reasons for wrong location are insufficient 
extension of palm, incorrect placement of palm, or 
insufficient separation of four fingers. If the position 
and posture of the palm are further standardized, the 
error positioning rate can be further reduced. The 
experimental results show that the above method is 
simple, effective, fast and can locate and score 
palmprints quickly and accurately. 
V. CONCLUSION 
In this paper, a new and purposeful exploration is 
made on the extraction of contour feature points in 
palmprint images, and the Harris corner detection 
algorithm is improved to realize the effective extraction 
of contour feature points. This algorithm avoids the 
need to determine the threshold artificially in the 
traditional corner detection algorithm, simplifies it, 
reduces the amount of calculation, grouping candidate 
corners and taking only one feature point in each group, 
thus improving the robustness of the algorithm, and 
International Journal of Advanced Network, Monitoring and Controls          Volume 04, No.01, 2019 
76 
provides a new and effective method for solving the 
problem of palmprint location in palmprint recognition. 
 
REFERENCE 
[1] Jain, R.Bole, S.Pankanti. Biometrics: Personal Identification in 
Networked Society. Kluwer Academic Publishers. 1999  
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Systems. Kluwer Academic Publishers. 2000 
[3] N.Duta, A.K.Jain, K.V.Mardia. Matching of Palmprint. Pattern 
Recognition Leters.2001, 23(4): 477-485 
[4] David Zhang. Automated Biometrics-Technologiesand Systems. 
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[5] A.Jain, R.Bolle, S.Pankanti .Biometrics: Personal identification in 
Networked Society. Kluwer Academic Publishers, 1999 
[6] WeiShu, D.Zhang. Palmprint verification: an implementation of 
biometric technology. IEEE International Conference on Pattern 
Recognition. 1998, 1:219-221 
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Recognition Leters.2001,23 (4):477-485 
[8] C.Han, H.Chen, C.Lin, K.Fan. Personal authentication using 
palm-print features. Pattern Recognition.2003,36 (2):371-381 
[9] C.Poon, D.C.M.Wong, H.C.Shen. A New Method in Locating and 
Segmenting Palmprint into Region-of-Interest Proceedings of the 
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Wavelet Model. IEEE Transactions on Information 
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English

Research on Harris Corner Detection Method in Palmprint Recognition System

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