2016 International Conference on Frontiers of Information Technology
Coin Recognition with Reduced Feature Set SIFT Algorithm Using Neural Network
Ghulam Farooque
Department of CS & IT
The University of Lahore
Lahore, Pakistan
ghulam.farooque@cs.uol.edu.pk
Imran Shafi
Department of Computer Science
Abasyn University Islamabad
Islamabad, Pakistan
imran.shafi@gmail.com
Allah Bux Sargano
Department of CS
COMSATS Institute of
Information Technology
Lahore, Pakistan
allahbux@ciitlahore.edu.pk
source (sensor or lens weakness). Another challenging
problem is that the coin may have some stain, hole or
physically in depth scratch on the surface area. Also, the
target object i.e. coin is not always found at a specified
region of the image. Accordingly, any technique proposed
to recognize coins must consider all these aspects and still
be efficient and robust enough to classify and recognize the
coins. Artificial Neural Network (ANN) is commonly used
as a classifier for the classification of objects in a digital
image. It has also been recommended for the classification
of coins and banknotes. Here we will discuss some existing
coins recognition techniques and related research efforts.
Various coins recognition techniques have been
proposed by many researchers in several countries, but still
a lot of challenges are there to recognize a coin in noisy,
scattered and low illumination environment. In [8], a
rotational invariant neural pattern recognition system for
coin recognition using yen and won coins is presented.
Like-wise in [9] a pattern recognition method for coins
identification and sorting was presented. In this method,
gray scale coin images were used as input to the
classification system. The coins were recognized from face
side only described by the recognition pattern set (RPS).
Similarly in [10], a method for coin recognition based on
image abstraction is presented; the abstract image is formed
from coin image based on strong edges. They used “canny”
operator to extract the edges of the coin image and features
were extracted by using spiral decomposition of pixels.
In [11], a method for coin recognition based on
direction of gradient vectors was proposed. The proposed
idea uses Generalized Hough Transform (GHT) for
segmentation of coin images and to estimate coin’s radius
and center. This approach is independent of contrast
changes and does not require edge extraction. The technique
used nth nearest neighbor scheme for classification of coins.
In [12], an experimental survey on coin classification
algorithms for ancient coins is presented. Some other
methods for ancient coin recognition based on shape and
local features were also proposed in [7, 12, 13]. In these
methods, images are segmented for separation of object
from its background then shape and local descriptors are
used to capture the characteristics of image for coin
identification. Another approach was proposed in [14] for
Indian coins recognition based on heuristic approach and
Hough transform. The heuristic approach is based on
different parameters like weight, shape, area, thickness, size,
average gray value, surface, design and diameter. Another
Abstract– Coin recognition is one of the prime important
activities for modern banking and currency processing
systems. These systems are widely used for coin sorting,
automatic counting, and vending machines. The technique at
the heart of such systems is object recognition in a digital
image. Object classification and recognition is still one of the
challenging research areas because we put our cognitive
capabilities in a computer system through an algorithm. The
reliability of such systems mainly depends on feature selection
and extraction mechanism. This paper presents a novel
approach for coins recognition. The proposed method uses
Scale Invariant Feature Transform (SIFT) algorithm to handle
the issues of rotations, scaling and illumination in a digital
image. This is followed by Principle Component Analysis
(PCA) for reducing extracted features set. This reduced
feature set is passed to feed forward back-propagation
artificial neural network (ANN) for classification and
recognition. The experimental results indicate that proposed
approach achieves state-of-the-art results for Pakistani coin
recognition.
Keywords – Coin Recognition; feature extraction; SIFT;
neural network
I.
INTRODUCTION
Object recognition is the process of identifying an
object being examined in a digital image for a set of known
features or labels. Humans have ability to identify a large
number of objects in any image with little efforts, even
when these appear in different perspective or rotated at
different degrees. Logically, objects are recognized from a
number of different perspectives (like color, size, shape or
appearance), in many different sizes. Object recognition
methods/techniques are employed in many real life
applications such as optical character recognition systems
[1, 2] chip defect finding systems, satellite data processing,
and image data mining to lot of industrial and commercial
application [3]. It is also used in medical imaging [4, 5],
defense and biometrics systems. In this research work, we
have applied object recognition techniques for coin
recognition. Coins recognition systems are used for banks,
hotels, supermarkets, etc. These systems are also used by
the organizations or institutions that deal with the ancient
coins.
Much work has been done for automatic recognition of
coins through digital image processing techniques [6, 7].
However, certain issues are quite challenging in coin
recognition systems. First challenge is handling the noise in
images due to bad illumination and/or faulty acquisition
978-1-5090-5300-1/16 $31.00 © 2016 IEEE
DOI 10.1109/FIT.2016.23
Waqar Ali
Department of CS & IT
The University of Lahore
Lahore, Pakistan
waqar.ali@cs.uol.edu.pk
93
method for Indian coin recognition was proposed in [15].
They used Hough transform and pattern averaging for
feature extraction followed by ANN for classification.
Another technique for ancient coin recognition is presented
in [16, 17]. They used a model which is based on
discriminative effect of coin images and spatial pooling with
co-occurrence encoding of visual words.
Our approach is based on a set of features extracted
using SIFT from the coin images. This feature set is then
reduced and most important features are selected using PCA
followed by a feed forward back-propagation ANN for
classification. The rest of the paper has been organized in
four sections. Section II describes the proposed approach. In
section III experimental results are presented and discussed.
Section IV concludes the paper.
II.
standard (reliable) acquisition source like digital
camera or OMR scanner.
2. Most of the object recognition systems [3] work with
the intensity image. Therefore we need to convert our
RGB color image to grayscale intensities. This may
also include noise removal and contrast enhancement
etc.
3. After preprocessing the intensity image is ready to
apply SIFT for feature extraction. Here, large feature
space is also reduced through PCA.
4. Reduced features are put into a neural network for
system training and classification.
5. Finally, experimental results of 200 Pakistani coins
are being presented.
A. Coin Image Acquisition
The first step would definitely be capturing coin’s
image for further processing. We also need a database for
training and testing purpose. Unfortunately, there is no such
standard database for Pakistani coins. We have collected
and scanned 200 coins with 3 different currency values (1
rupee, 2 rupees, and 5 rupees). These 200 coins have been
scanned with 200 dpi (dots per inch) resolutions; 24-bit
picture scan mode with jpeg image type. In order to verify
robustness of the proposed system, our database includes
both scanned and camera captured images. Some of the
scanned and camera captured images are shown in Figure 2
and Figure 3 respectively.
PROPOSED SYSTEM
Our proposed system is based on feature extraction
through Scale Invariant Features Transform (SIFT)
algorithm for Pakistani coins recognition. The architecture
diagram of the system is shown in Figure 1.
Coin Image Acquisition
RGB to Grayscale Transformation
Features Extraction Through SIFT
Recognition / Classification through NN Training
1
2
3
4
5
6
7
8
9
10
Results and Discussions
Figure 1. Architecture of coin recognition system
Figure 2. Scanned Pakistani coins of different worth; (1) head of Rs. 5 coin,
(2) tail of Rs. 5 coin, (3) head of Rs. 2 coin (1st version), (4) tail of Rs. 2
coin (1st version), (5) head of Rs. 2 coin (2nd version), (6) tail of Rs. 2 coin
(2nd version), (7) head of Rs. 1 coin (1st version), (8) tail of Rs. 1 coin (1st
version), (9) head of Rs. 1 coin (2nd version), (10) tail of Rs. 1 coin (2nd
version)
SIFT algorithm extracts feature points with respect to
scale, affine transformation, illumination 3D view point and
noise [14]. Since SIFT extract maximum features from the
object image and in our case we should not retain large
feature space [18]. Therefore, we have applied Principle
Components Analysis (PCA) to reduce feature space [19,
20]. In the end, these reduced features are passed to a neural
network for classification and training.
For a given grayscale or RGB image we have proposed
a five step procedure to input process and conclude the coin
recognition. Our approach detects and recognizes the given coin
by means of following algorithm.
Algorithm-1
1. Image acquisition is almost being the first step for
any image processing or objects recognition system.
As a first step we capture coin images through some
Figure 3. Coins Captured with Camera
Since object detection in noisy and low contrast images
with complex background is one of the key challenges for
94
such systems in computer vision [10, 15]. Therefore, a set of
noisy, low contrast and blur images have been included in
sample space as shown in Figure 3. These images are
comparatively ambiguous and are even difficult to recognize
for the human vision. These images will be helpful for
testing the reliability and robustness of our proposed
technique.
B. RGB to Grayscale Transformation
As a second step of our proposed model we need to
convert input 24-bit RGB images into 8-bit grayscale
images. Processing images in three or four separate channels
is quite complex and challenging [21]. In order to reduce the
processing time, RGB image should be converted to 8-bit
grayscale intensity image [22].Therefore, we have cropped
and converted input images into 8-bit grayscale images. All
grayscale images (1 rupee, 2 rupees, and 5 rupees) are
resized as 100 × 100 pixel images. These grayscale images
will be used as input for features extraction. The different
steps of pre-processing are shown in Figure 4.
Read
the Coin
image
Crop the
image
Convert RGB
image into
grayscale image
Figure 5. Features extraction process suing SIFT
The SIFT algorithm wasp proposed by David Lowe
[37] in 1999 for the detection of local features in a digital
image. It extracts features often called SIFT key points from
the image in a circular region with an orientation. It is
illustrated by four parameters as shown in Figure 5, the key
point
center
coordinates x and y,
its scale,
and
its orientation. The SIFT detects key points from the image
structures at multiple scales and rotation positions. The
SIFT selected key points are invariant to translation,
rotations, and rescaling of the image. SIFT features have
been employed in many applications such as for face
recognition [38], pose estimation of autonomous grasping in
robotic arm system [39] and sign language recognition
systems [40]. For facilitation, we have designed following
algorithm for features extraction from coin images
Algorithm-2
1. Read coin image as I = imread (pre-processed images)
2. Apply SIFT to extract features , as F = SIFT (I) | F∈
Z+
3. Apply PCA on F to extract most common features as
C = PCA (F) | C ⊇ F
4. Store C in a features database D for system training
There is another critical element, due to rotation
invariant SIFT return a large feature space for each image. It
is quite complex to analyze all these extracted features;
therefore we have used Principle Component Analysis
(PCA) to reduce features space. It improves the efficiency
of entire system by putting most common features for
further analysis. As directed in Algorithm-2, some sample
images with extracted features are presented in Figure 6
Resize the
image to
100 × 100
Figure 4. Image Pre-processing
C. Features Extraction through SIFT
For the human vision this is so simple to understand
and describe a complete story from a single image [23]. On
the other hand, a computer program can also discover
semantic concepts from digital images but it is quite
difficult task for the computer system. The first prime
activity for an intelligent system in technical understanding
is to extract efficient and effective visual features from a
digital image [24, 25]. As we know, the most common
visual features include color, texture and shape [26-31].
Many techniques have been proposed for features extraction
from a digital image [32-34], each one has its own pros and
cons. Also, an efficient method for face detection may not
necessary to be as much efficient in symmetry detection.
In our case, it is important to consider here that coins
are round in shape as shown in Figure 2. Therefore, we
cannot make sure a fix degree (in term of rotation) of coins
during the image acquisition process. Due to the rotation
invariant, extracted features should not be same for a same
coin at different degree of rotation [35]. It creates lot of
complication and challenges for the classifier algorithm to
detect and classify the object in right direction. In this
regard, the Scale Invariant Features Transform (SIFT)
algorithm have a good discrimination power [18, 36] to
extract rotation invariant features.
Figure 6. Feature points detection using SIFT
95
experimental results indicate 99% accurate training of the
Neural Network
D. Reduction of feature space through PCA
The extracted features are quite large, hence complex for
classification and analysis. Principal component analysis
(PCA) is one of the statistical techniques frequently used for
reduction of a large data set [20, 41-43]. For the reduction of
data with n dimensions, PCA aims to find a linear subspace
of d dimension less than n such that the data points lie
mainly on this linear subspace. The output reduced subspace
attempts to maintain the most common members of the
original data. In our case, we would like to reduce the large
number of features extracted in section 2.3 into a small
features space so that the training process of recognition
system could be simple, fast and more relevant.
E.
Recognition / Classification through NN
After features extraction, we require a classifier algorithm
for further processing. Number of well-known classifiers
exists for classification in data mining and object
recognition [44]. Neural Network (NN) is one of the most
widely used classifier for object recognition techniques [45].
By architecture, there are two types of Neural Network
classification, (i) Feed-forward Neural Network also called
bottom-up or top-down and (ii) Feed-back Neural Networks
architecture also called interactive or recurrent. In Feedforward neural network inputs travel one way where each
layer forwards information to the next layer. There is no
feedback in this type of network. In Feed-back Neural
network input (signals) travel in both directions (bidirectional). We have selected Feed-forward Neural
Network as a classifier for our coin recognition system. The
structure of the NN is shown in Fig. 7
Figure 8. Training Confusion Matrix
TABLE 1. Neural network learning parameters
F.
No. of coins images
100
Coins types
3
Hidden neurons
25
Inputs
256
Output neurons
3
Maximum iterations
1000
Validation
Neural Network training dataset is divided into three
parts. 80% coin images are being used for training of Neural
Network, 10% coin images are used for validation part and
10% coin images are used for test part. Validation results
show smooth process of Neural Network training. All errors
(training, validation, and test) are reducing together shown
in figure 9, it means training was successful and system is
able to recognize the unseen samples of same kind.
Figure 7. Neural Network Structure
The feature vector is passed as input to ANN for
training; the database of 200 coin images is divided into two
parts. 100 images are used for neural network training and
the remaining 100 images are used for testing of the coins
recognition system. The training dataset of recognition
technique consists of 100 coin images (1, 2, and 5 rupees
coins), Table1 shows the learning parameters of Neural
Network training. Training results are shown in confusion
matrix in Figure 8. There are three classes from 1 to 3. Class
one stands for 1 rupee coins, class two stands for 2 rupees
coins, and class three stands for 5 rupee coins. Our
Figure 9. Training Performance
III.
RECOGNITION RESULTS
After completion of the Neural Network training, we
saved the trained network on the computer. 100 coin images
of different worth/denomination are used to test the
96
performance and accuracy of the trained neural network,
100 coin images include three types of Pakistani coins
which consist on clean and noisy coin images .Recognition
system is tested in two parts, in the first part each type of
coins are passed separately to the system including 1, 2, and
5 rupees coins. Table 2 shows the recognition results of
proposed system. As, shown in Table 2 system has 84%
recognition ability
In the Second part 100 coin images of all types
including (1, 2, and 5 rupees) are passed as input to the
recognition system. These 100 coin images are new to the
recognition system; results show that the system is able to
recognize all the types of Pakistani coins and the recognition
rate is 84%. The confusion matrix is shown in figure 10
there are three classes from 1 to 3. Class one indicates 1
rupee coins, class two indicates 2 rupees coins, class three
indicates 5 rupees coins.
This makes the system reliable and suitable for real time
applications.
REFERENCES
[1]
[2]
[3]
[4]
[5]
[6]
[7]
[8]
[9]
Figure 10. Test Confusion Matrix
TABLE 2. Coins recognition results
Coin Type
No. of Coins
tested
No. of coins
recognized
1 PKR Coin
40
36
90%
2 PKR Coin
40
31
77.5%
5 PKR Coin
20
17
85%
IV.
[10]
Recognition
ability
[11]
CONCLUSION
[12]
In this research work we have proposed a
comparatively smart method for coins recognition using
SIFT features extraction and Neural Network classifier.
Experimental results are more than satisfactory. Pakistani
coins are considered as objects of recognition, there are
three types of Pakistani coins (1rupee, 2rupees, and
5rupees). In this system coins are scanned from one side
(Tail) and also coins are captured with digital camera and
set of SIFT features are extracted based on variances. PCA
is applied on SIFT features to reduce the feature space. For
evaluating the performance of the recognition system,
experiment has been done on 200 coins images. With
reduced SIFT features 84% recognition rate is achieved.
[13]
[14]
[15]
[16]
97
M. Ejiri, "Machine vision in early days: Japan’s
pioneering contributions," in Asian Conference on
Computer Vision, 2007, pp. 35-53.
S. Kashioka, et al., "A transistor wire-bonding system
utilizing multiple local pattern matching techniques,"
IEEE Transactions on Systems, Man, and Cybernetics,
pp. 562-570, 1976.
A. Andreopoulos and J. K. Tsotsos, "50 years of object
recognition: Directions forward," Computer Vision and
Image Understanding, vol. 117, pp. 827-891, 2013.
G. Gallus, "Contour analysis in pattern recognition for
human chromosome classification," Abdce, vol. 2, pp.
95-108, 1968.
G. GALLUS and G. REGOLIOSI, "A decisional model
of recognition applied to the chromosome boundaries,"
Journal of Histochemistry & Cytochemistry, vol. 22, pp.
546-553, 1974.
Y. Yadav and A. Sood, "A Comparative Survey on
Various Coin Recognition Systems Based on Image
Processing," International Journal of Engineering and
Computer Science, vol. 2, pp. 3272-3277, 2013.
M. Kampel and M. Zaharieva, "Recognizing ancient
coins based on local features," in International
Symposium on Visual Computing, 2008, pp. 11-22.
M. Fukumi, et al., "Rotation-invariant neural pattern
recognition system with application to coin recognition,"
IEEE Transactions on neural networks, vol. 3, pp. 272279, 1992.
M. Nölle, et al., "Dagobert-a new coin recognition and
sorting system," in Proceedings of the 7th Internation
Conference on Digital Image Computing-Techniques
and Applications (DICTA’03), Syndney, Australia, 2003.
A. Chalechale, "Coin recognition using image
abstraction and spiral decomposition," in Signal
Processing and Its Applications, 2007. ISSPA 2007. 9th
International Symposium on, 2007, pp. 1-4.
M. Reisert, et al., "An efficient gradient based
registration technique for coin recognition," in Proc. of
the Muscle CIS Coin Competition Workshop, Berlin,
Germany, 2006, pp. 19-31.
M. Zaharieva, et al., "Image based recognition of ancient
coins," in International Conference on Computer
Analysis of Images and Patterns, 2007, pp. 547-554.
R. Huber-Mörk, et al., "Numismatic object identification
using fusion of shape and local descriptors," in
International Symposium on Visual Computing, 2008,
pp. 368-379.
C. Velu and P. Vivekanandan, "Indian Coin Recognition
System of Image Segmentation by Heuristic Approach
and Houch Transform (HT)," Int. J. Open Problems
Compt. Math, vol. 2, 2009.
S. Modi and D. Bawa, "Automated Coin recognition
system using ANN," arXiv preprint arXiv:1312.6615,
2013.
H. Anwar, et al., "Ancient Coin Classification Using
Reverse Motif Recognition: Image-based classification
[17]
[18]
[19]
[20]
[21]
[22]
[23]
[24]
[25]
[26]
[27]
[28]
[29]
[30]
[31]
[32]
[33]
[34]
of Roman Republican coins," IEEE Signal Processing
Magazine, vol. 32, pp. 64-74, 2015.
H. Anwar, et al., "Coarse-grained ancient coin
classification using image-based reverse side motif
recognition," Machine Vision and Applications, vol. 26,
pp. 295-304, 2015.
G. T. Flitton, et al., "Object Recognition using 3D SIFT
in Complex CT Volumes," in BMVC, 2010, pp. 1-12.
W. Xu and E.-J. Lee, "Face Recognition Using Wavelets
Transform and 2D PCA by SVM Classifier,"
International Journal of Multimedia and Ubiquitous
Engineering, vol. 9, pp. 281-290, 2014.
Y. Lu, et al., "Feature selection using principal feature
analysis," in Proceedings of the 15th ACM international
conference on Multimedia, 2007, pp. 301-304.
M. Cui, et al., "Color-to-gray conversion using
ISOMAP," The Visual Computer, vol. 26, pp. 13491360, 2010.
T. Kumar and K. Verma, "A Theory Based on
Conversion of RGB image to Gray image," International
Journal of Computer Applications, vol. 7, pp. 7-10,
2010.
R. C. Gonzalez and R. E. Woods, "Image processing,"
Digital image processing, vol. 2, 2007.
L. Shen, et al., "Statictics of Gabor features for coin
recognition," in 2009 IEEE International Workshop on
Imaging Systems and Techniques, 2009, pp. 295-298.
D. ping Tian, "A review on image feature extraction and
representation techniques," International Journal of
Multimedia and Ubiquitous Engineering, vol. 8, pp. 385396, 2013.
T. K. Shih, et al., "An intelligent content-based image
retrieval system based on color, shape and spatial
relations,"
PROCEEDINGS-NATIONAL
SCIENCE
COUNCIL REPUBLIC OF CHINA PART A PHYSICAL
SCIENCE AND ENGINEERING, vol. 25, pp. 232-243,
2001.
C.-F. Tsai, "Image mining by spectral features: A case
study of scenery image classification," Expert Systems
with Applications, vol. 32, pp. 135-142, 2007.
P. Stanchev, et al., "High level color similarity retrieval,"
2003.
N.-C. Yang, et al., "A fast MPEG-7 dominant color
extraction with new similarity measure for image
retrieval," Journal of Visual Communication and Image
Representation, vol. 19, pp. 92-105, 2008.
M. M. Islam, et al., "A geometric method to compute
directionality features for texture images," in 2008 IEEE
International Conference on Multimedia and Expo,
2008, pp. 1521-1524.
S. Li and J. Shawe-Taylor, "Comparison and fusion of
multiresolution features for texture classification,"
Pattern Recognition Letters, vol. 26, pp. 633-638, 2005.
S. Bane and D. Pawar, "Survey on Feature Extraction
methods in Object Recognition."
G. Mandloi, "A Survey on Feature Extraction
Techniques for Color Images," International Journal of
Computer Science and Information Technologies, vol. 5,
pp. 4615-4620, 2014.
V. Singh, et al., "A Comparative Study on Feature
Extraction Techniques for Language Identification,"
[35]
[36]
[37]
[38]
[39]
[40]
[41]
[42]
[43]
[44]
[45]
98
International Journal of Engineering Research and
General Science, 2014.
P. Thumwarin, et al., "A robust coin recognition method
with rotation invariance," in 2006 International
Conference on Communications, Circuits and Systems,
2006, pp. 520-523.
H. Akbar, et al., "Bilateral symmetry detection on the
basis of Scale Invariant Feature Transform," PloS one,
vol. 9, p. e103561, 2014.
D. G. Lowe, "Object recognition from local scaleinvariant features," in Computer vision, 1999. The
proceedings of the seventh IEEE international
conference on, 1999, pp. 1150-1157.
D. R. Kisku, et al., "Probabilistic approach to face
recognition," Journal of the Chinese Institute of
Engineers, vol. 35, pp. 529-534, 2012.
C.-H. Chen and H.-P. Huang, "Pose estimation for
autonomous grasping with a robotic arm system,"
Journal of the Chinese Institute of Engineers, vol. 36, pp.
638-646, 2013.
A. Tharwat, et al., "Sift-based arabic sign language
recognition system," in Afro-european conference for
industrial advancement, 2015, pp. 359-370.
R. E. G. Valenzuela, et al., "Dimensionality reduction
through PCA over SIFT and SURF descriptors," in
Cybernetic Intelligent Systems (CIS), 2012 IEEE 11th
International Conference on, 2012, pp. 58-63.
L. Juan and O. Gwun, "A comparison of sift, pca-sift and
surf," International Journal of Image Processing (IJIP),
vol. 3, pp. 143-152, 2009.
Y. Ke and R. Sukthankar, "PCA-SIFT: A more
distinctive representation for local image descriptors," in
Computer Vision and Pattern Recognition, 2004. CVPR
2004. Proceedings of the 2004 IEEE Computer Society
Conference on, 2004, pp. II-506-II-513 Vol. 2.
C. Xu, "Research of Coin Recognition Based on
Bayesian Network Classifier," Advances in Information
Sciences & Service Sciences, vol. 4, 2012.
A. Khashman, et al., "Intelligent coin identification
system," in 2006 IEEE Conference on Computer Aided
Control System Design, 2006 IEEE International
Conference on Control Applications, 2006 IEEE
International Symposium on Intelligent Control, 2006,
pp. 1226-1230.