Extend Authentication Using Sensor Technique
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Content
IS J AA
Extend Authentication Using Sensor Technique
International Journal of Systems , Algorithms & Applications
Suresh Kumar Kashyap1, Vikas Chandra Pandey2, Pooja Agrawal3, Minakshi Agrawal4 1,2,3,4Lect. of IT Department, Dr. C.V. Raman Univercity Kargiroad Kota,Bilaspur(C.G.) 1 [email protected] ,2 [email protected], 3 [email protected], [email protected] e-mail:
Abstract - Purpose of this research paper is to identify a person through written handwriting authentication this thesis focuses on the written handwriting authentication. Biometry offers potential for automatic personal verification and differently from other means for personal verification; biometric means are not based on the possession of anything or the knowledge of some information. Of the various biometrics, handwritingbased verification has the advantage that handwriting analysis requires no invasive measurement and is widely accepted since handwriting has long been established as the most diffuse mean for personal verification in our daily life, including commerce applications, banking transactions, automatic fund transfers, etc. Keywords— Sensor, Biometrics, SVD, Data Gloves, Handwriting
II. PRESENT AUTHENTICATIO SYSTEM The problem of personal identification is multiplied when computer comes into the communication channel of two parties. For this reason, more reliable authentication scheme is needed to build up the required trust of communication link. Password, PINs and token are examples of traditional authentication technology.
I. INTRODUCTION In early days, human beings were commonly identified by their names. As the human population increased, method of identifying a person became more sophisticated. People needed to be associated with more information such as family’s background, nationality, gender, age and blood group to label each and every human being as the unique person in the world. The problem of personal identification is multiplied when computer comes into the communication channel of two parties. For this reason, more reliable authentication scheme is needed to build up the required trust of communication link. Password, PINs and token are examples of traditional authentication technology. Biometry offers potential for automatic personal verification and differently from other means for personal verification; biometric means are not based on the possession of anything or the knowledge of some information. Of the various biometrics, handwriting-based verification has the advantage that handwriting analysis requires no invasive measurement and is widely accepted since handwriting has long been established as the most diffuse mean for personal verification in our daily life, including commerce applications, banking transactions, automatic fund transfers, etc. A wide variety of feature extraction and classification methods have been applied to the handwriting recognition. Two categories of verification systems are usually distinguished: off-line and online systems for hand written handwriting authentication and verification.
Fig. 1 Example of present authentication system
However, these methods have major drawbacks as passwords and PINs tend to be forgotten or shared out whereas token can be easily lost or stolen. On the web use Various CAPTCHA, Handwritten CAPTCHA, n the context of an http transaction, the basic access authentication is a method designed to allow a web browser, or other client program, to provide credentials in the form of a user name and password– when making a request.
Fig. 2 Example of present authentication system
Volume 2, Issue 1, January 2012, ISSN Online: 2277-2677
17
Extend Authentication Using Sensor Technique
IS J AA
International Journal of Systems , Algorithms & Applications
This is very normal form of authenticate user online or offline using username and password. But this is not a secure way to authenticate a real user. Biometrics use human behavior to authenticate a real user. III. PROPROSED AUTHENTICATION TECHNIQUE Handwriting recognition authenticates identity by measuring handwritten handwritings. The handwriting is treated as a series of movements that contain unique biometric data, such as personal rhythm, acceleration, and pressure flow. Unlike electronic handwriting capture, which treats the handwriting as a graphic image, handwriting recognition technology measures how the handwriting is signed. In a handwriting recognition system, a person signs his or her name on a digitized graphics tablet or personal digital assistant. The system analyzes handwriting dynamics such as speed, relative speed, stroke order, stroke count, and pressure. The technology can also track each person’s natural handwriting fluctuations over time. The handwriting dynamics information is encrypted and compressed into a template. Off-line approaches for handwriting recognition: In offline systems for which the handwriting is captured once the writing processing is over, and thus only a static image is available. As for offline handwriting verification processing, most of the earlier work involves the extraction of features from the handwritings image by various schemes.used local grid features and global geometric features to build multi-scale verification functions. Savoring et al. Used an extended shadow code as a feature vector to incorporate both local and global information into the verification decision. B. Fang et al. used positional variances of the 1 dimensional projection profiles of the handwriting patterns and the relative stroke positions of two-dimensional patterns used a quasimultiresolution technique using GCS (Gradient, Structural and Concavity) features for feature extraction. On-line approaches to handwriting recognition: Input devices in this category are either digitizing tables or smart pens and hand gloves. In digitizing table-based systems both global and local features that summarize aspects of handwriting shape and dynamics of handwriting production are used for handwriting verification. In Pen-based systems a smart pen is used to collect data such as pen-tip positions, speeds, accelerations, or forces while a person is signing. The invisible pen-up parts of the handwriting are used to construct a handwriting verification system. Trajectories left in pen-up situation, called ‘‘virtual strokes,’’ are used to extract the optimal features, which represent the personal characteristics of the authentic handwriting and affect the error rate greatly.
Utilizing the multiple degrees of freedom offered by the data glove for each finger and the hand, a novel on-line handwriting verification system using the Singular Value Decomposition (SVD) numerical tool for handwriting classification and verification is presented. The proposed technique is based on the Singular Value Decomposition in finding r singular vectors sensing the maximal energy of glove data matrix A, called principal subspace, so the effective dimensionality of A can be reduced. Having modeled the data glove handwriting through its rprincipal subspace, handwriting authentication is performed by finding the angles between the different subspaces. A demonstration of the data glove is presented as an effective high-bandwidth data entry device for handwriting verification. This SVD-based handwriting verification technique is tested and its performance is shown to be able to recognize forgery handwritings with a false acceptance rate of less than 1.2%. IV. DATA GLOVES Data glove is a new dimension in the field of handwriting verification, which can reflect the identity of a person and that renders the forging process nearly impossible. Glove handwriting is a virtual reality- based environment to support the signing process and it offers multiple degrees of freedom for each finger and for the hand as well. The dynamic features of the data glove provide information on: 1. Patterns distinctive to an individuals’ handwriting and hand size. 2. Time elapsed during the signing process 3. Hand trajectory dependent rolling Thus, the glove as a tool for handwriting recognition allows authentication of people not only through the biometric characteristics of their handwritings but also through the size of their hands. Figure 1 shows the data glove and location of sensors. shows signals from the data glove during signing process. In our research, we proposed a new approach for handwriting verification using 14 sensor-based data glove. The technique is based on correlation measure through the use of Singular Value Decomposition (SVD) of different handwriting groups.
Fig. Sensor mappings for 5DT data glove 14 ultra
Volume 2, Issue 1, January 2012, ISSN Online: 2277-2677
18
Extend Authentication Using Sensor Technique
IS J AA
International Journal of Systems , Algorithms & Applications
V. SVD-BASED HANDWRITING VERIFICATION TECHNIQUE Singular value decomposition (SVD) provides a new way for extracting algebraic features from the handwriting data using data glove. SVD has been used in many fields such as data compression, signal processing, and control system and pattern analysis. Consider a data glove of m sensors each generates n samples per handwriting, producing an output data matrix, A(m×n). Usually n >> m, where m denotes the number of measured channels while n denotes the number of measurements. The properties of the singular values are described in detail in the following: Theorem 1. Singular Value Decomposition- If AÎ Rmxn , then there exists the diagonal matrices [ ] mxm m U = u ,L,u Î R 1 and [ ] nxn n V = v ,L, v Î R 1 so that ( , ) 1, p T U AV = diag s L s where min( , ), 0 1 2 = ³ ³ L ³ ³ p p m n s s s , i 1,2,...p, i s = are the singular values of A . The singular values are the square roots of eigenvalues I l of H AA or A A H, that is i i s = l Theorem 2. The stability of Singular Value: Assume , , mxn mxn mxn A B Î R and their singular values are , , 1 2 n 1 2 n s ³s ³ L ³s t ³t ³ L ³t respectively, then 2 A B i i s −t £ − . This means that there is a disturbance at A, the variation of its singular values is not more than the 2 . -norm of the disturbance matrix. Theorem 3. The scaling property- If singular values of mxn A are k s s ,s 1, 2,L , the singular values Of mxn a * A are * * 2 * 1 , , , k s s L s , then ( , , , ) ( , , , ) * * 2 * 1 2 k 1 k a s s L s=ssLs Theorem 4. The rotation invariant property- If P is the unitary matrix, then the singular values of PA are the same as those of A. The above properties of SVD are very desirable in handwriting verification, when handwriting data are taken using data glove. VI. DISTANCE MEASUREMENT FOR HANDWRITING DATA SETS By establishing the properties of SVD, we extracted the first r left singular vectors (U) of glove data matrix A, which is account for most of the variation in the original data. This means that with m × n data matrix that is usually largely over determined with much more samples (columns) than channels (rows): n >> m the singular value decomposition allows to compact the most of handwriting characteristics into r vectors. Now, having identified each handwriting through its r-th . principal subspace, the authenticity of the tried handwriting can be obtained by calculating the Euclidean
distance between its principal subspace and the genuine reference. The Euclidian distance for every genuine or forged handwriting i k X x , x ,..., x 1 2 with the reference handwriting i k Y y , y ,..., y 1 2 is calcu lated by given equation:
VII. MODEL FOR THE SVD-BASED SIGNATURE VERIFICATON TECHNIQUES Enrollment phase: · Use data glove to provide the system with 10 genuine samples of his/her signature. · Out of the collected 10 genuine samples select the reference signature. · Extract the r-principal subspace of the reference signature and save it in the database for matching. Verification phase: · Use data glove to input the signature of the user (one sample). · Calculate the r-principal subspace of the claimed identity using SVD. · Match the principal subspace of the claimed identity to the enrolled models in the database through the similarity factor. · Compare the similarity factor with the decision threshold for ACCEPT or REJECT. VIII. CONSLUSION In this research paper we have summarized and critically discussed the main issues to be taken into account for the evaluation of the accuracy and performance of signature verification technique. The real-time signature identification and verification is necessary in most practical application. Our proposed SVD-based signature verification technique can process glove-based signature data in high speed and obtained a significant result. Its effectiveness and significant performance has been proven by the experiments. In order to compare the proposed technique using data glove with other on-line signature verification technique, the equal error rate value of the SVDbased signature verification technique is calculated and proved to be significantly lower (EER = 2.46%) than the other on-line techniques. Hence, from the experimental point of view, our proposed technique for signature identification and verification need low computational time as well as produce high level of accuracy. So, the SVD-based signature verification technique using data glove may represent a new trend in real-time signature verification system.
Volume 2, Issue 1, January 2012, ISSN Online: 2277-2677
19
Extend Authentication Using Sensor Technique
IS J AA
International Journal of Systems , Algorithms & Applications
ACKNOWLEDGMENT The authors would like to take this opportunity to thank Vikas Chandra Pandey for his invaluable peer review and editorial work on this article. Her insights were truly appreciated and this paper would not have been possible without her assistance REFERENCES
[1] R. Plamondon, G. Lorette, “Automatic signature verification and writer identi.cation—the state of the art”, Pattern Recognition, vol. 1, no. 2, pp. 107–131, 1989. [2] A.K. Jain, F.D. Griess, S.D. Connell, On-line signature verification, Pattern Recognition 35 (2002) 2963–2972. [3] H. Lei, V. Govindaraju, A comparative study on the consistency of features in on-line signature verification, Pattern Recognition Letters. 26 (2005) 2483–2489. [4] C. Chang, W. Tsai. “Model-based analysis of hand gestures from single images without using marked gloves or attaching marks
on hands”, Proc. of the Fourth Asian Conference on Computer Vision (ACCV2000), pp. 923-930, 2000. [5] S. Sayeed, R. Besar, and N. S. Kamel, "Dynamic signature verification using sensor based data glove", Proc. of 8th 2390, International Conference on Signal Processing, IEEE Press, pp. 2387-2006. [6] S. Sayeed, N. S. Kamel, and R. Besar, “Biometric Personal Authentication Based on Handwritten Signature”, Proc. of the 3rd International Colloquium on Signal Processing and its Applications, pp. 34-39, 2007. [7 ] http://www.5dt.com/products/pdataglove14.html [8 ] http://www.biometricgroup.com. [9] http://www.cadix.com. [10] http://www.5dt.com/products/pdataglove14.html [11]. R. Kashi, J. Hu, W.L. Nelson and W. Turin. “A hidden markov model approach to on-line handwritten signature verification”, International Journal on Document Analysis and Verification, no.1, pp. 102–109, 1998.
Volume 2, Issue 1, January 2012, ISSN Online: 2277-2677
20
Extend Authentication Using Sensor Technique
International Journal of Systems , Algorithms & Applications
Suresh Kumar Kashyap1, Vikas Chandra Pandey2, Pooja Agrawal3, Minakshi Agrawal4 1,2,3,4Lect. of IT Department, Dr. C.V. Raman Univercity Kargiroad Kota,Bilaspur(C.G.) 1 [email protected] ,2 [email protected], 3 [email protected], [email protected] e-mail:
Abstract - Purpose of this research paper is to identify a person through written handwriting authentication this thesis focuses on the written handwriting authentication. Biometry offers potential for automatic personal verification and differently from other means for personal verification; biometric means are not based on the possession of anything or the knowledge of some information. Of the various biometrics, handwritingbased verification has the advantage that handwriting analysis requires no invasive measurement and is widely accepted since handwriting has long been established as the most diffuse mean for personal verification in our daily life, including commerce applications, banking transactions, automatic fund transfers, etc. Keywords— Sensor, Biometrics, SVD, Data Gloves, Handwriting
II. PRESENT AUTHENTICATIO SYSTEM The problem of personal identification is multiplied when computer comes into the communication channel of two parties. For this reason, more reliable authentication scheme is needed to build up the required trust of communication link. Password, PINs and token are examples of traditional authentication technology.
I. INTRODUCTION In early days, human beings were commonly identified by their names. As the human population increased, method of identifying a person became more sophisticated. People needed to be associated with more information such as family’s background, nationality, gender, age and blood group to label each and every human being as the unique person in the world. The problem of personal identification is multiplied when computer comes into the communication channel of two parties. For this reason, more reliable authentication scheme is needed to build up the required trust of communication link. Password, PINs and token are examples of traditional authentication technology. Biometry offers potential for automatic personal verification and differently from other means for personal verification; biometric means are not based on the possession of anything or the knowledge of some information. Of the various biometrics, handwriting-based verification has the advantage that handwriting analysis requires no invasive measurement and is widely accepted since handwriting has long been established as the most diffuse mean for personal verification in our daily life, including commerce applications, banking transactions, automatic fund transfers, etc. A wide variety of feature extraction and classification methods have been applied to the handwriting recognition. Two categories of verification systems are usually distinguished: off-line and online systems for hand written handwriting authentication and verification.
Fig. 1 Example of present authentication system
However, these methods have major drawbacks as passwords and PINs tend to be forgotten or shared out whereas token can be easily lost or stolen. On the web use Various CAPTCHA, Handwritten CAPTCHA, n the context of an http transaction, the basic access authentication is a method designed to allow a web browser, or other client program, to provide credentials in the form of a user name and password– when making a request.
Fig. 2 Example of present authentication system
Volume 2, Issue 1, January 2012, ISSN Online: 2277-2677
17
Extend Authentication Using Sensor Technique
IS J AA
International Journal of Systems , Algorithms & Applications
This is very normal form of authenticate user online or offline using username and password. But this is not a secure way to authenticate a real user. Biometrics use human behavior to authenticate a real user. III. PROPROSED AUTHENTICATION TECHNIQUE Handwriting recognition authenticates identity by measuring handwritten handwritings. The handwriting is treated as a series of movements that contain unique biometric data, such as personal rhythm, acceleration, and pressure flow. Unlike electronic handwriting capture, which treats the handwriting as a graphic image, handwriting recognition technology measures how the handwriting is signed. In a handwriting recognition system, a person signs his or her name on a digitized graphics tablet or personal digital assistant. The system analyzes handwriting dynamics such as speed, relative speed, stroke order, stroke count, and pressure. The technology can also track each person’s natural handwriting fluctuations over time. The handwriting dynamics information is encrypted and compressed into a template. Off-line approaches for handwriting recognition: In offline systems for which the handwriting is captured once the writing processing is over, and thus only a static image is available. As for offline handwriting verification processing, most of the earlier work involves the extraction of features from the handwritings image by various schemes.used local grid features and global geometric features to build multi-scale verification functions. Savoring et al. Used an extended shadow code as a feature vector to incorporate both local and global information into the verification decision. B. Fang et al. used positional variances of the 1 dimensional projection profiles of the handwriting patterns and the relative stroke positions of two-dimensional patterns used a quasimultiresolution technique using GCS (Gradient, Structural and Concavity) features for feature extraction. On-line approaches to handwriting recognition: Input devices in this category are either digitizing tables or smart pens and hand gloves. In digitizing table-based systems both global and local features that summarize aspects of handwriting shape and dynamics of handwriting production are used for handwriting verification. In Pen-based systems a smart pen is used to collect data such as pen-tip positions, speeds, accelerations, or forces while a person is signing. The invisible pen-up parts of the handwriting are used to construct a handwriting verification system. Trajectories left in pen-up situation, called ‘‘virtual strokes,’’ are used to extract the optimal features, which represent the personal characteristics of the authentic handwriting and affect the error rate greatly.
Utilizing the multiple degrees of freedom offered by the data glove for each finger and the hand, a novel on-line handwriting verification system using the Singular Value Decomposition (SVD) numerical tool for handwriting classification and verification is presented. The proposed technique is based on the Singular Value Decomposition in finding r singular vectors sensing the maximal energy of glove data matrix A, called principal subspace, so the effective dimensionality of A can be reduced. Having modeled the data glove handwriting through its rprincipal subspace, handwriting authentication is performed by finding the angles between the different subspaces. A demonstration of the data glove is presented as an effective high-bandwidth data entry device for handwriting verification. This SVD-based handwriting verification technique is tested and its performance is shown to be able to recognize forgery handwritings with a false acceptance rate of less than 1.2%. IV. DATA GLOVES Data glove is a new dimension in the field of handwriting verification, which can reflect the identity of a person and that renders the forging process nearly impossible. Glove handwriting is a virtual reality- based environment to support the signing process and it offers multiple degrees of freedom for each finger and for the hand as well. The dynamic features of the data glove provide information on: 1. Patterns distinctive to an individuals’ handwriting and hand size. 2. Time elapsed during the signing process 3. Hand trajectory dependent rolling Thus, the glove as a tool for handwriting recognition allows authentication of people not only through the biometric characteristics of their handwritings but also through the size of their hands. Figure 1 shows the data glove and location of sensors. shows signals from the data glove during signing process. In our research, we proposed a new approach for handwriting verification using 14 sensor-based data glove. The technique is based on correlation measure through the use of Singular Value Decomposition (SVD) of different handwriting groups.
Fig. Sensor mappings for 5DT data glove 14 ultra
Volume 2, Issue 1, January 2012, ISSN Online: 2277-2677
18
Extend Authentication Using Sensor Technique
IS J AA
International Journal of Systems , Algorithms & Applications
V. SVD-BASED HANDWRITING VERIFICATION TECHNIQUE Singular value decomposition (SVD) provides a new way for extracting algebraic features from the handwriting data using data glove. SVD has been used in many fields such as data compression, signal processing, and control system and pattern analysis. Consider a data glove of m sensors each generates n samples per handwriting, producing an output data matrix, A(m×n). Usually n >> m, where m denotes the number of measured channels while n denotes the number of measurements. The properties of the singular values are described in detail in the following: Theorem 1. Singular Value Decomposition- If AÎ Rmxn , then there exists the diagonal matrices [ ] mxm m U = u ,L,u Î R 1 and [ ] nxn n V = v ,L, v Î R 1 so that ( , ) 1, p T U AV = diag s L s where min( , ), 0 1 2 = ³ ³ L ³ ³ p p m n s s s , i 1,2,...p, i s = are the singular values of A . The singular values are the square roots of eigenvalues I l of H AA or A A H, that is i i s = l Theorem 2. The stability of Singular Value: Assume , , mxn mxn mxn A B Î R and their singular values are , , 1 2 n 1 2 n s ³s ³ L ³s t ³t ³ L ³t respectively, then 2 A B i i s −t £ − . This means that there is a disturbance at A, the variation of its singular values is not more than the 2 . -norm of the disturbance matrix. Theorem 3. The scaling property- If singular values of mxn A are k s s ,s 1, 2,L , the singular values Of mxn a * A are * * 2 * 1 , , , k s s L s , then ( , , , ) ( , , , ) * * 2 * 1 2 k 1 k a s s L s=ssLs Theorem 4. The rotation invariant property- If P is the unitary matrix, then the singular values of PA are the same as those of A. The above properties of SVD are very desirable in handwriting verification, when handwriting data are taken using data glove. VI. DISTANCE MEASUREMENT FOR HANDWRITING DATA SETS By establishing the properties of SVD, we extracted the first r left singular vectors (U) of glove data matrix A, which is account for most of the variation in the original data. This means that with m × n data matrix that is usually largely over determined with much more samples (columns) than channels (rows): n >> m the singular value decomposition allows to compact the most of handwriting characteristics into r vectors. Now, having identified each handwriting through its r-th . principal subspace, the authenticity of the tried handwriting can be obtained by calculating the Euclidean
distance between its principal subspace and the genuine reference. The Euclidian distance for every genuine or forged handwriting i k X x , x ,..., x 1 2 with the reference handwriting i k Y y , y ,..., y 1 2 is calcu lated by given equation:
VII. MODEL FOR THE SVD-BASED SIGNATURE VERIFICATON TECHNIQUES Enrollment phase: · Use data glove to provide the system with 10 genuine samples of his/her signature. · Out of the collected 10 genuine samples select the reference signature. · Extract the r-principal subspace of the reference signature and save it in the database for matching. Verification phase: · Use data glove to input the signature of the user (one sample). · Calculate the r-principal subspace of the claimed identity using SVD. · Match the principal subspace of the claimed identity to the enrolled models in the database through the similarity factor. · Compare the similarity factor with the decision threshold for ACCEPT or REJECT. VIII. CONSLUSION In this research paper we have summarized and critically discussed the main issues to be taken into account for the evaluation of the accuracy and performance of signature verification technique. The real-time signature identification and verification is necessary in most practical application. Our proposed SVD-based signature verification technique can process glove-based signature data in high speed and obtained a significant result. Its effectiveness and significant performance has been proven by the experiments. In order to compare the proposed technique using data glove with other on-line signature verification technique, the equal error rate value of the SVDbased signature verification technique is calculated and proved to be significantly lower (EER = 2.46%) than the other on-line techniques. Hence, from the experimental point of view, our proposed technique for signature identification and verification need low computational time as well as produce high level of accuracy. So, the SVD-based signature verification technique using data glove may represent a new trend in real-time signature verification system.
Volume 2, Issue 1, January 2012, ISSN Online: 2277-2677
19
Extend Authentication Using Sensor Technique
IS J AA
International Journal of Systems , Algorithms & Applications
ACKNOWLEDGMENT The authors would like to take this opportunity to thank Vikas Chandra Pandey for his invaluable peer review and editorial work on this article. Her insights were truly appreciated and this paper would not have been possible without her assistance REFERENCES
[1] R. Plamondon, G. Lorette, “Automatic signature verification and writer identi.cation—the state of the art”, Pattern Recognition, vol. 1, no. 2, pp. 107–131, 1989. [2] A.K. Jain, F.D. Griess, S.D. Connell, On-line signature verification, Pattern Recognition 35 (2002) 2963–2972. [3] H. Lei, V. Govindaraju, A comparative study on the consistency of features in on-line signature verification, Pattern Recognition Letters. 26 (2005) 2483–2489. [4] C. Chang, W. Tsai. “Model-based analysis of hand gestures from single images without using marked gloves or attaching marks
on hands”, Proc. of the Fourth Asian Conference on Computer Vision (ACCV2000), pp. 923-930, 2000. [5] S. Sayeed, R. Besar, and N. S. Kamel, "Dynamic signature verification using sensor based data glove", Proc. of 8th 2390, International Conference on Signal Processing, IEEE Press, pp. 2387-2006. [6] S. Sayeed, N. S. Kamel, and R. Besar, “Biometric Personal Authentication Based on Handwritten Signature”, Proc. of the 3rd International Colloquium on Signal Processing and its Applications, pp. 34-39, 2007. [7 ] http://www.5dt.com/products/pdataglove14.html [8 ] http://www.biometricgroup.com. [9] http://www.cadix.com. [10] http://www.5dt.com/products/pdataglove14.html [11]. R. Kashi, J. Hu, W.L. Nelson and W. Turin. “A hidden markov model approach to on-line handwritten signature verification”, International Journal on Document Analysis and Verification, no.1, pp. 102–109, 1998.
Volume 2, Issue 1, January 2012, ISSN Online: 2277-2677
20
