Currency Characteristic Extraction and Identification Research Based on

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1 Currency Characteristic Extraction and Identification Research Based on PCA and BP Neural Network Bu-Qing CAO, Jian-Xun LIU, 2 Bin Wen School of Computer Science and Engineering& Key Laboratory of Knowledge Management and Network-based Manufacture, Hunan University of Science and technology, Xiangtan, 420, China 2 School of Information Science and echnology, Hainan Normal University, Haikou,5758,China Abstract In recent years, Principle Component Analysis is an extraction method for statistics characteristic, which has been more researched and widely used in the signal processing, pattern recognition, digital image processing and other fields. his paper mainly describe that original currency characteristic vectors will be carried the linear transform by Principle Component Analysis Method, and then reduced-dimension original currency characteristic vector is automatically classified by BP Neural Networks, and finally identification research experiment is made for different kinds of currency,such as yuan, 5 yuan, 0 yuan and 20 yuan. he experiment results indicate that currency characteristic extraction and identification algorithm based on Principle Component Analysis and BP neural network has higher identification rate and better identification effect. Introduction Keywords: PCA, BP, Currency Identification With the rapid development of world economy, people increasingly pursue better efficiency and automation in commercial activities. he currency automation identification has become a new research hotspot, especially in retail and banking domain. herefore, it is a major task to rapidly improve the efficiency of millions of systems worldwide at low costs. Some researchers of Japan and other countries have researched currency identification with earlier time, and related research results have been successfully applied to currency recognition of Yen, Dollar, Euro and hai Baht. As a result, a variety of recognition algorithms and related technology have developed, such as differences inequality techniques [] and neural network technology [2, 3]. Among them, differences inequality techniques need to include the characteristics difference point and threshold of each currency, which are all requires the choice of expert based on own experience, and involve the factors of subjective lookup, which are lack of systematic plan, its experiment time is very long, and so lead to many repetitive work on characteristic search with manual operation, especially in the new currency (such as the new 50 Yuan). In addition, the surface status of currency is complex, for example, the abrasion, contamination and defect of currency because of its long-term circulating process. herefore, the recognition result is not satisfactory. Neural network pattern recognition method is a new research point of pattern recognition field in recent years. Because of parallel processing capability, self-organization and generalization of neural network, it is widely used in many aspects on system research. Fourier power spectrum of currency have been regarded as the input of neural network in referenced literature 4 and 8, which make network scale is very large, the time of train and recognition is long, and so cannot meet the requirements of real-time equipment and the implementation process of specific algorithm is also difficult. A real time paper currency recognition method is proposed by Liu Jia-feng[4], which extracts the directional block characteristics and the size information of a currency image as the recognition characteristics. Liu Li proposes a novel approach for serial number extracting and recognizing base on RBF neural network, which have been applied in Paper Currency Sorting System, and the experiment shows that the recognition system runs stably and accurately, the collected image shows clearly [5]. Kalyan Kumar Debnath proposes a paper currency recognition system using negatively correlated neural network ensemble [6]. Junfang Guo proposes a reliable method for paper currency recognition based on LBP, with the advantages of simplicity and high speed, and the experimental results show that this improved method has a high recognition rate, as well as robustness for noise and illumination change [7]. MS. Journal of Convergence Information echnology(jci) Volume7, Number2, February 202 doi:0.456/jcit.vol7.issue2.5 38

2 .P proposes characteristic extraction parameters for genuine paper currency recognition &verification [8].In this paper, a technique for recognizing paper currencies of different countries has been discussed, which uses three characteristics of paper currencies including size, color, and template. o sum up, making use of neural network to identify currency has become a hot spot. However, it is very important how to effectively choose typical characteristics of currency to make neural networks structure simple, and so improve the training and learning performance of neural network, enhance identification ability to and efficiency, these researches are very little. Based on my foregoing research results [9, 0], this paper mainly describe that linear transform for original currency characteristic vector is carried out by Principle Component Analysis Method, and then reduced-dimension primitive currency characteristic vector is automatically classified by BP Neural Networks, and finally identification research experiment is done for different kinds of currency, such as yuan, 5 yuan, 0 yuan and 20 yuan. 2 he Obtainment of Sample Data he first step of making use of neural network to build application model is obtain the original currency data, it is also very important and critical step. he authenticity and validity of original currency data is the precondition of effective learning and training for neural network. here are a variety of currencies owing to different country and par value, and they all have different characteristics mark, such as par value, authenticity, which are need to collect a variety of characteristics signal to identify them. In this paper, RMB is the identification object. Firstly, personal computer and currency acceptor will be connected by a serial cable. Secondly, RMB will be put in currency acceptor, making use of developed Database Learning Control Platform (Fig.) to send RS-232 protocol instructions to currency acceptor, red, infrared and fluorescence data information collected by currency acceptor will be read into personal computer. Finally, these data information will be saved as a file with suffix BDB. he saved data information of single file is consisting of some gray values of 024 pixel of a RMB (its value is form 0 to 255).After handling these data information, including the extraction of useful information and removal of useless information, collected data information of a RMB is gray values of 64 pixel. In other words, the data dimensions of neural network training are 64. Currency identification is to identify these saved data information of file to distinguish par value and authenticity. When using neural network to carry classification identification, there are two ways, which are characterless extraction and characteristic extraction. Because neural network have the ability to handle a large number of low-level information, you can put the entire image into neural network to identify, that is characterless extraction way. his way makes the preprocessing of identification simpler and system design easier. However, in practice, if neuronal connections amount are very large, the training of neural network will become difficult. hus, the scale of the neural network has some limitations. Meanwhile, as the size of RMB is very large, it is impossible that all data information of the entire RMB image is inputted into neural network to identify, and so characteristic extraction will be selected in this paper. Figure. Database Learning Control Platform 39

3 3 Currency characteristic Extraction and Selection By foregoing analysis, if 64 dimension data of original currency sample (RMB) are inputted into neural network, the training time of neural network is very long and the structure of it is very complex. Even if all weights and thresholds are good trained for neural network, recognition process will spend a lot of running time when system enter actual recognition stage. So, suppose that correctly identify currency, the long processing time is unacceptable. herefore, a characteristic extraction method that is Principle Component Analysis Method will be selected in this paper. It will choose data matrix of original currency sample based on its covariance matrix, carry many operation and effectively extract the characteristic of these data information to input neural network, which not only greatly improve the training and learning performance of neural network, but also enhance identification ability to and efficiency. he data of original currency sample is divided into two major parts: One is the training data sample containing 60 RMB, the other is the test data sample containing 60 RMB. So, Principle Component Analysis Method will be carried for these two sets of data sample, specific steps are as follows[]: () Select the training data sample containing 60 RMB, respectively are yuan, 5yuan, 0yuan and 20yuan, and their amount are all. he two-dimensional data information of them will be converted into one-dimensional vector. First of all, 60 RMB images will be expressed by 60 matrixes. As the collected data information of each RMB is gray values of 64 pixels (its value is form 0 to 255), converted one-dimensional vector is as follows: I [ a, a, a,..., a ] () i In that way, each RMB can be expressed as Ii( i,2,...,60). According to kinds of RMB, that are yuan, 5yuan, 0yuan and 20yuan, they can be expressed a set of vectors I [ I, I2, I3,..., I].hus, the mean vector of various types of RMB, for example yuan, can be obtained as follows: I ave I (2) i N i Furthermore, the difference between one-dimensional vector of each RMB and its mean vector will be gained as follows: Ai Ii Iave (3) hus, the entire training data sample can be expressed as follows: A [ A, A2, A3,..., A] (4) (2) Construct the covariance matrix, the eigenvalue and eigenvectors of it will be calculated according to the K-L transform. Firstly, constructed covariance matrix is as follows: C A ia, i.e. i C A A (5) i Secondly, construct projection characteristic space by calculated eigenvalue and eigenvectors of covariance matrix C. he eigenvalue and eigenvectors of each RMB will be calculated, which build a covariance matrix C with 64*64 dimensions by a RMB variable matrix A with 64* dimensions. So, 64 eigenvalue that is i ( i,2,...,64) and 64 eigenvectors that is Ui( i,2,...,64) will be obtained. Finally, according to the descending order 2..., the corresponding eigenvectors are 64 U, U,..., 2 U 64 will constitute K-L transform matrix, that is each line of matrix U is compose of eigenvectors of covariance matrix C, and the first line of matrix U is the eigenvectors of corresponding eigenvalue. Among this, U [ U, U 2,..., U 64]. (3) Select the principle components of the training data sample and project them to characteristic space.

4 he anterior m principle components are y, y 2,..., y, and their cumulative variance m contribution rate is as follows: m 64 i (6) k ( m) [ / ] 00% When the cumulative variance contribution rate of the anterior m principle components is enough large, such as ( m) 80%, extracted eigenvectors of the training data sample are justly equal to the anterior m principle components. he anterior m principle components can be calculated and obtained as follows: Y U I (7) Among this, U [ U, U 2,..., U 64], I [ I, I2, I3,..., I], Y [ y, y 2,..., y m ]. Here, set the cumulative variance contribution rate ( m) equal to 90%.Based on the obtained eigenvalue results, we select the anterior 6 bigger eigenvectors, that is m equal to 6. For example, the calculated eigenvalue and corresponding variance contribution rate of yuan is as follows able. able.he Calculated Eigenvalue and Corresponding Variance Contribution Rate of Yuan Eigenvalue Specific Value Variance Contribution Rate 64.26% 4.83% 9.2% 3.22% 2.8%.34% According to above able, the cumulative variance contribution rate ( m) of those 6 eigenvalues equal to 95.04%, that is more than 90%.hen, we can select the corresponding eigenvectors of them to calculate the principal components and gain final 6*0 dimensions data matrix Y whose values will be input neural network for training: Y U I, U [ U, U 2,..., U 6], Y [ y, y,..., y ] (8) 2 6 hus, according to the same steps above, the principal components of 5 yuan, 0 yuan and 20 yuan can be calculated. he experiments indicate that the corresponding cumulative variance contribution rates ( m) are all more than 90% when the number of the principal components is 6(that is m equal to 6).So, the number of principal components of yuan, 5 yuan, 0 yuan and 20 yuan are all 6, which all can represent the main information provided by the original training data sample, and form new training data sample by them. he form method of new testing data sample containing 60 RMB, respectively are yuan, 5yuan, 0yuan and 20yuan, and their amount are all, which is same to those of new training data sample. he above data processing has been realized by MALAB7.0, the specific process is divided into three steps: Firstly, calculate the covariance matrix by the formula ;Secondly, the eigenvalue and eigenvectors of matrix C will be gained by the C A A 0 formula [ v, d] eig( C), and the 6 principal components will be selected according to their cumulative variance contribution rate ( m).meanwhile, the anterior 6 column of eigenvectors matrix (that is the biggest 6 eigenvalues) will be selected and constitute new eigenvectors matrix U, and obtain its transpose U. Among this, v represents eigenvector and d represents eigenvalue. Finally, the new training data sample and the new testing data sample will be gained by the formulay U I. 4

5 4 he Design of BP Neural Networks Classifier After currency original data has been disposed by principle component analysis method, the new training data sample and the new testing data sample will be gained. Next, we can input them to BP neural network model and carry currency recognition simulation[2]. 4.. he Sample raining Existing neural network theory prove that three feed-forward neural network can approximate any nonlinear relationship under any precision condition, and thus three-layer BP neural network structure will be selected in this paper. Because the dimension of characteristic disposed by principle component analysis method is 6, the number of input nodes of BP neural network is 6.he number of output nodes of it is 4, and the desired output values respectively are when input the 6 characteristics of yuan, when input those of 5yuan, when input those of 0yuan, when input those of 20yuan. he neurons transformation function of BP neural network hidden layer is set to tansig type and those of BP neural network output layer is set to purelin type. In order to distinguish classification results, the boundary value is defined as 0.85.When there is a value of 4 output nodes is more than 0.85, corresponding currency will be identified. If all values of 4 output nodes are all less than 0.85, corresponding currency will be identified as false currency. For example, if the output values respectively are , corresponding currency will be identified as 5yuan.In addition, the desired error value of BP neural network is set to 0.00, learning rate is set to 0.0, and the maximum training times of it is set to he selection of the hidden layer nodes of BP neural network is a more complex problem. If the number of the hidden layer nodes is too little, it is possible that network training is infeasible, the samples no-studied previously cannot be identified, and fault tolerance is poor. If the number of the hidden layer nodes is too more, the study time of sample is long and the error value does not the best. Here, the initial value of the hidden layer nodes is set to 5 according to the experiential formula A = SQR (BC) and the final value of those will be determined by considering the convergence speed and output accuracy of BP neural network. Among this, A represents the number of hidden layer nodes, B represents the number of input layer nodes, and C represents the number of output layer nodes. In specific experiment, the number of hidden layer nodes is selected from 5 to 0, the corresponding convergence times of BP neural network are as follows able2. able2. he Corresponding Convergence imes of BP Neural Network for Different Hidden Layer Nodes he number of hidden layer nodes he convergence times No convergence From the above able2, We can know that the corresponding convergence times of BP neural network is the least when the number of hidden layer nodes is 8, and its training error curve is also better. So, the final number of hidden layer nodes of BP neural network is 8, that is the structure of BP neural network is consist of 6 input layer nodes,8 hidden layer nodes and 4 output layer nodes. After the training parameters and structure of BP neural network are determined, weights and thresholds of it can be initialized with the random number from - to, and then the training process can be carried making use of BP algorithm, the training error curve of it can be shown as follows Fig2. 42

6 0 2 Sum-Squared Network Error for 7 Epochs 0 Sum-Squared Error Epoch Figure 2.he raining Error Curve of BP Algorithm he whole running time of BP neural network is seconds, and its actual output and desired output are as follows able 3. able3. he Actual Output and Desired Output of BP Neural Network Currency Kind actual output desired output yuan yuan yuan yuan he esting Results and Analysis he trained weights and thresholds of the hidden layer and output layer of BP neural network will be saved and regarded as the new initial weights and thresholds of BP neural network after accomplishing the sample training. hen, the new training data sample containing 60 RMB and the new testing data sample containing 60 RMB will be tested and their identification results are as follows able4( Among this, Identification Rate =he number of Correct currency identification / otal number of raining (or esting ) Data Sample ). able4. BP Neural Network Identification Results Currency Kind yuan 5 yuan 0 yuan 20 yuan he new training data sample Number: Number: Number: Number: Correct Wrong Correct Wrong Correct Wrong Correct Wrong Identification Rate 00% 00% 00% 00% he new testing data sample Number: Number: Number: Number: Correct Wrong Correct Wrong Correct Wrong Correct Wrong Identification Rate 90% 92.5% 85% 97.5% From the above able4, We can know that the total identification rate of the new testing data sample is 9.25%. here are many reasons for wrong identification results. First of all, the face situation of currency is complex. Some currency is serious polluted, others is very old or incomplete, which 43

7 result in identification rate decreasing. he solution for those is to try to collect various types of currency to expand possible currency identification scope during the learning and training.secondly, the collected data of currency is limited because there are electromagnetic, vibrant interference during the collection process of the original currency data. he solution for those is to expand the collection scope or collect the more data which represent the mainly characteristic of currency, to reflect the whole currency information. At the same time, hardware s performance of currency identification can be improved to resist interference. 5 Conclusions and Next Research Work Principal component analysis method is the best transform of data compression in the minimum mean square error, which has the effect of reducing correlation and highlighting difference. It make the linear transform to abandon the smaller variance components and reserve the bigger variance components, and optimal extract the principal component in the minimum mean square error. In this paper, we propose a currency characteristic extraction and identification method based on principal component analysis and BP neural network. he experiment results indicate this method have the higher identification rate and the better identification effect. In next research work, it is emphasis that consider experiment comparison with other currency identification method. 6 Acknowledgements We thank the anonymous referees for their comments and suggestions which improved the quality of the paper. his work is supported by National Natural Science Foundation of China under grant No ; the Program for New Century Excellent alents in University of China under grant No. NCE-0-0;the Scientific Research Fund of Hunan Provincial Education Department of China under grant No. 09K085, No.09C. 7 References [] F.akeda,S.Omatu,.Inoue, An Expert System for Determing the Discrimitive Functions for Bill Money Recognition, rans.info.process. Soc.Japan, vol.33, no.7, pp , 992. [2] F.akeda,S.Omatu, High-speed Paper Currency Recognition by Neural Networks, IEEE rans.neural Networks, no.6, pp.73-77,995. [3] A.Frosimi,M.Gori,P.Priami, A Neural Network-based Model for Paper Currency Recognition and Verification, IEEE ransaction on Neural Networks,vol.7,no.6,pp ,996. [4] LIU Jia Feng, LIU Song Bo, ANG Xiang Long, An Algorithm of Real-ime Paper Currency Recongnition, Journal of Computer Research and Development, vol.7, pp , [5] Liu Li,Ye Yu-tang,Xie Y,Pu Liang, Serial Number Extracting and Recognizing Applied in Paper Currency Sorting System Based on RBF Network, Proceedings of 200 International Conference on Computational Intelligence and Software Engineering, Wuhan, CHINA, pp.-4,200. [6] Kalyan K D, Sultan U A, Md. S, Kazuyuki M, A Paper Currency Recognition System Using Negatively Correlated Neural Network Ensemble, Journal of Multimedia,vol.5, no.6,pp , 200. [7] Junfang Guo,Yanyun Zhao,Anni Cai, A reliable method for paper currency recognition based on LBP, Proceedings of 200 2nd IEEE International Conference on Network Infrastructure and Digital Content, pp ,200. [8] JinYe, Liu Songbo, Liu Jiafeng, Song Ling, and ang Xianglong, An Edge-Based Defect Detection Algorithm for Paper Currency, Journal of Computer Research and Development, vol.44, no.2, pp , [9] Bu-Qing Cao, Jian-Xun Liu, Currency Recognition Modeling Research Based on BP Neural Network Improved by Gene Algorithm, International Conference on Computer Modeling and Simulation, SanYan, China, pp , 200. [0] Bu-Qing Cao, Ou Jin, Jian-Biao He, Design and Implementation of Algorithm of Currency Recognition Based on BP Nerve Network, Computer Measurement & Control, vol.5, no.4, pp , [] Seyed Zeinolabedin Moussavi, Saeedreza Ehteram, Ali Sadr, he New Face Recognition echnique With the use of PCA and LDA, JCI: Journal of Convergence Information echnology, vol. 3, no. 2, pp.34-38, [2] Shifei Ding, Weikuan Jia, Chunyang Su, Xiaoliang Liu, Jinrong Chen, An Improved BP Neural Network Algorithm Based on Factor Analysis, JCI: Journal of Convergence Information echnology, vol. 5, no. 4, pp ,