OFFLINE SIGNATURE VERIFICATION

Transcription

1 International Journal of Electronics and Communication Engineering and Technology (IJECET) Volume 8, Issue 2, March - April 2017, pp , Article ID: IJECET_08_02_016 Available online at ISSN Print: and ISSN Online: IAEME Publication OFFLINE SIGNATURE VERIFICATION Dr. M. Narayana Professor, Dept. of ECE, Jaya Prakash Narayan College of Engineering, Mahabubnagar, Telangana, India L. Bhavani Annapurna Associate Professor, Dept. of ECE, Jaya Prakash Narayan College of Engineering, Mahabubnagar, Telangana, India K. Mounika B. Tech, Final year, Dept. of ECE, Jaya Prakash Narayan College of Engineering, Mahabubnagar, Telangana, India ABSTRACT In the era of growing technology, security is the major concern to avoid fake and forgeries. It is also one of the most easily forgeable biometric identity when compared to other biometric features like thumb impression, face recognition etc. Now a day s Signature verification is one of the most important features for checking the authenticity of a person. The classification of the feature utilizes statistical features. Our proposed model has three stages: image pre-processing, feature extraction and classification and verification. Scanned signatures are introduced into the computer, our proposed method modifies their quality by image enhancement and noise reduction, to be followed by feature extraction in which we extract threshold, mean of bounding box, perimeter, total pixel count, ratio of height and width, area out of which threshold is efficient and combination of those properties leads in better results and finally used Euclidean distance model for classification of signature either genuine or forgery. Key words: Forgeries, Signature Verification, Euclidean Distance Model, Recognition Cite this Article: Dr. M. Narayana, L. Bhavani Annapurna and K. Mounika, Offline Signature Verification, International Journal of Electronics and Communication Engineering and Technology, 8(2), 2017, pp editor@iaeme.com

2 Offline Signature Verification 1. INTRODUCTION Signature verification is a biometric verification which is an important research area targeted at automatic identity verification such as legal, banking and high security environments.[1] Signature verification can be divided into two classes online and offline. Online approach uses a stylus and electronic tablet. Stylus which is connected to a computer to extract information about a signature and dynamic information like pressure, speed of writing, velocity etc which is used for verification purpose. Offline signature verification involves less electronic control and uses signature images captured by scanner or camera. In this features are extracted from scanned signature image and these signature images captured simple [2]. Difficulty lies in the fact that it is hard to segment signature strokes due to highly stylish and unconventional writing styles and non-repetitive nature of variation of signature because of age, illness and emotional state of a person [3]. When we couple all these it causes a large variation. A robust system has to be designed that should consider all these types of factors but also detect various type of forgery. System should neither be too coarse nor too sensitive. It should have acceptable trade-off between low False Acceptance Rate and a low False Rejection Rate Types of forgery There are various types of signature forgeries, some of them are: 1. Random Forgery: This is also known as simple forgery and is very easy to detect. The signer creates a signature in his own style by just knowing the name of an individual whose sign is to be made. 2. Unskilled Forgery: The signer creates a signature after observing the signature once or twice without any prior experience. 3. Skilled Forgery: The signer may be a professional in copying signatures. He creates a signature after having a good practice over it. Such signatures are most difficult to detect. 2. OBJECTIVES We need to be overcome four problems for better results. They are: To extract the Region of Interest (ROI). To find the angle of inclination of the user signature. To remove the noise in the signature. Eliminating the effects of scaling factor. 3. METHODOLOGY In order to design a system, which will detect the forged signatures by comparing some special features with original one, the following architecture has been proposed editor@iaeme.com

3 Dr. M. Narayana, L. Bhavani Annapurna and K. Mounika Figure 1 Algorithm of offline signature verification system 3.1. Image acquisition In the offline signature verification system individual person signature are taken on a paper but the signature to be preprocessed by the system must be in digital format. Hence we use a scanner or a camera for image acquisition in which we scan the user signature which is in the paper form and after scanning it will be in digital format and now it is used for preprocessing Pre-Processing It is performed to improve the quality of signature image and to verify the signature correctly, preprocessing of acquired signature is required. In this stage, we will perform binarization, noise elimination, size normalization, extracting the region of interest, image thinning and image rotation. Binarization As we know there is no importance of color in which the signer is signing that s why we convert the image from color image to gray scale image. Then for future calculation we have converted that image to binary image using threshold which is found using OTSU s method. In binarization a color image is converted into binary image so as to make feature extraction easier because it is required to compare only two pixel values (colors). It allows us to reduce the amount of image information (removing color and background), so the output image is black-white. The black-white type of the image is much easier for further processing. Noise removal The acquired sign may sometimes contain noise because while scanning some noise will be included because of the issues in the scanning device and extra pen dots other than signature. It is necessary to remove those extra pixels from acquired image to verify the signature correctly. In this phase, the noise associated with the image need to be removed. This can be done using median filter editor@iaeme.com

4 Offline Signature Verification Thinning The signer may use different pen at different time and that s why thickness of the signature may vary and so as to eliminate the thickness differences thinning is to be performed and it makes the image one pixel thick. It improves the accuracy rate and also the computational time is reduced. Finding the bounding box of the image and cropping the image After previous step it is required to locate the extract position of the signature in the image to perform signature verification, because signature can be anywhere on the paper and they never start from the same position and neither do they terminate at the same position and they can sign in different angles and sizes. Now the 1 st problem is to find the exact position of the signature from the paper and the 2 nd problem is to find the angle of the signature. After that the necessary correction need to be done in the context mentioned above. To solve the 1 st problem a solution has been proposed. And the solution is to scan the signature and find the smallest rectangular area covering the total signature. It is achieved using bounding box property from regionprops command and after that the required portion is extracted. Angular problem solution Another important task is that the angular detection of a user. To compare the signature signed in different angles that is stored in database we need to overcome this problem. And to solve this angular problem firstly we need to find the endpoints of an image and some mathematical formula is used. After finding endpoints, for image rotation to be done here we need to use co-ordinate geometry, to find the angle and to rotate the image accordingly. Those two end points are considered as point a and point b. Now we get x1, y1 co-ordinates from point a and x2, y2 co-ordinates from point b. we need to draw a straight line between these two points and then we need to find the angle between the line joining the end points and the horizontal x-axis and then the image is to be rotated by the angle to make it horizontal to x- axis. tanθ= () () θ=tan^-1 () () (1) (2) Size normalization After image rotation is performed the size of the signature image gets enlarged hence size normalization is required for the comparison of two images. In this method we need to fix the height and width of the image so that it remains constant and comparison can be performed Feature extraction Feature extraction techniques take vital role to improve the accuracy of signature verification system. Similar characteristics of a signature are called features of that signature and accurately extract those features are called extraction. In this we have used threshold, number of connected components, and mean of bounding box, perimeter and aspect ratio. Aspect ratio is defined as the ratio of width and height of signature. The individual property efficiency and the elapsed time are shown in the below table 1. Threshold: We perform thresholding using OTSU s method editor@iaeme.com

5 Dr. M. Narayana, L. Bhavani Annapurna and K. Mounika OTSU s method In computer vision and image processing, OTSU s method is used to automatically perform histogram shape-based image thresholding. The algorithm assumes that the image to be threshold contains 2 classes of pixels (e.g. foreground and background then calculates the optimum threshold separating those 2 classes. Connected component Pen up and pen down are one of the most important properties that a person follows while signature. And this feature could be difficult to copy for a fake person. This feature of a signature is extracted by counting the number of stroke in a signature. The line between pen up and pen down is said to be a stroke. Height/width ratio Height and width of signature is obtained after drawing a smallest rectangle in which signature can fit. Height and the width of a signature remain same for a same person. The size of the signature might differ in different time but the ratio of height and width remain approximately same for same person Signature verification This process identifies and differentiates a person s signature from another and here we have used Euclidean distance to verify the signature. This classifier is good for features extracted and fast in computation. This is simple distance Euclidean distance model by following equation. In threshold calculation these distances are useful. We can calculate distance (d) by using the equation below: Distance (d) = Sqrt( ( ) ) (3) If the Euclidean distance equals to zero then the test image is considered as the matched original image otherwise it is treated as forged signature. 4. RESULTS AND DISCUSSIONS 4.1. FACTORS OF PERFORMANCE EVALUATION False Acceptance Ratio (FAR) and False Rejection Ratio (FRR) are the two parameters used for measuring the performance of any signature verification method: The false acceptance ratio is given by the number of fake signatures accepted by the system with respect to the number of comparisons made. The values of FAR are obtained when a sample genuine database is tested upon by false signatures. The lower the value of FAR the better it is and FAR is evaluated by FAR= ( ) ( ) 100 (4) The false rejection ratio is the total number of genuine signatures rejected by the system with respect to the number of comparisons made. The values of FRR are obtained when a sample false database is tested upon by genuine signatures. The lower the value of FRR the better it is and FRR is evaluated by $%%= ( '( ) ) ( '( ) 100 (5) editor@iaeme.com

6 Offline Signature Verification 4.2. Performance analysis of proposed work Table 1 Values of FAR and FRR S. No Genuine database Forged database FAR FRR False Acceptance Ratio (FRR) and False Rejection Ratio (FRR) are the two parameters used for measuring the performance of any signature verification method. The lower the value of FAR the better it is and the value is 0 and it indicates it is best. The lower the value of FRR the better it is and the value is 0 and it indicates it is best. Table 2 Results of individual properties PROPERTY EFFICIENCY ELAPSED TIME Threshold Ratio of height and width Perimeter Mean of bounding box Number of connected components The following experiments are part of our results. Table 1 shows the individual property results. Obviously threshold alone has higher efficiency when compared to other properties Test images 4.4. Database images (a) (b) (c) (d) Figure 2 Test images (a) (b) (c) (d) editor@iaeme.com

7 Dr. M. Narayana, L. Bhavani Annapurna and K. Mounika (e) (f) (g) (h) Figure 3 Database images The above images are taken as samples from the database there are twenty seven different genuine signatures and sixty seven forged signatures available in datasets. We need to use this datasets for signature verification RESULTS Figure 4 Output 4.6. COMPARATIVE ANALYSIS We have performed all the possible combinations and out of which the effective combination of properties with their efficiency and computational time are shown below: Table 3 Combined results Properties Efficiency Elapsed time 1, ,3, ,2,4, ,2,3,4, Foot note: 1- Threshold, 2-Mean of bounding box, 3- Area, 4-Perimeter, 5-Ratio of height and width, 6-Total pixel count editor@iaeme.com

8 Offline Signature Verification 5. CONCLUSION The algorithm developed by us, uses various statistical features to characterize signatures that effectively serve to distinguish signatures of different persons and also it can detect the three types of forgeries i.e., random forgery, unskilled forgery and skilled forgery. From the performance evaluating factors FAR and FRR we can also say that this is the best algorithm for signature verification and as we have overcome the problems due to angle of inclination and region of interest and scaling factors it can be said as the modified off-line signature verification. 6. FUTURE SCOPE As the database increases the computational time of offline signature verification system increases. And the dynamic information is lost using this method. Dynamic information is much more efficient than the static information which we have used in this. Hence as a future work we can design a system which is a combination of static and dynamic information i.e., the combination of offline and online verification systems. Or we can design an On-line signature verification system which is purely based on dynamic features which is much more efficient than this. REFERENCES [1] Off-line signature verification Mrs. Tulsi Gupta Student of M. Tech (weekend Programme) IT, Guru Gobind Singh Indraprastha University, Dwaraka. [2] Offline signature verification using Grid based and Centroid based approach (Sayantan Roy, Department of Computer Science Engineering), ISM Dhanbad Jharkhand. [3] ISS (Aug 25, 2013) Offline Signature Verification system with Gaussian Mixture Models (GMM) Charu Jain1, Priti Singh2, Preeti Rana3. [4] Signature verification, Dr. H.B.Kekre et.al/ International Journal on Computer Science and Engineering (IJCSE). [5] Online Signature Recognition Using Matlab, International Journal of Innovative Research in Electrical, Electronics, Instrumentation and Control Engineering Vol. 3, Issue 2, February [6] Offline Signature Verification for Detecting Signature Forgery: A Comparative Study, International Journal on Computer Trends and Technology (IJCTT)-Volume 21 Number 3-Mar [7] A Review Paper on Signature Recognition, International Journal for Research in Applies Science & Engineering Technology (IJRASET), vol. 3, Issue VI, June 2015, ISSN: [8] An Automatic Offline Signature Verification and Forgery Detection System. [9] Hand Written Signature recognition & Verification, International Journal of Advanced Research in Computer Science and Software Engineering, Vol 3, Issue 3, March [10] A Survey for offline signature verification and recognition techniques using image processing, Volume 1, Spl. Issue 2 (May, 2014). [11] Enhanced signature verification and recognition using MATLAB, International Journal of Innovative Research in Advanced Engineering (IJIRAE), vol 1, issue 4 (May 2014). [12] Offline handwritten signature identification and verification using feature point extraction, International Journal on Computer Applications ( ), vol 141-No 14, May [13] Offline signature verification and identification using distance statistics, International journal of pattern recognition and Artificial Intelligence, vol. 18, No 7, (2004) editor@iaeme.com

9 Dr. M. Narayana, L. Bhavani Annapurna and K. Mounika [14] Handwritten signature verification ECE-533-Project by Ashish dhawan and Aditi R. Ganesan. [15] Extraction of global features for off line signature recognition, Faculty of computer science, Bialystok Technical University. [16] A survey on handwritten signature verification techniques, IJARCSMS, vol 3, issue 1, January [17] Improved offline signature verification scheme using feature point extraction method. [18] Enhanced signature verification and recognition using MATLAB, International Journal of nnovative Research in Advanced Engineering (IJIRAE), Volume 1, issue 4 (may 2014). [19] A Review Paper on Signature Recognition, International Journal for Research in Applies Science & Engineering Technology (IJRASET), Volume 3, Issue VI, June 2015, ISSN: [20] Padmajadevi G and Dr. Aprameya K.S, Offline Handwritten Signature Recognition Using Edge hinge and Edge extraction Techniques and Manhattan Distance Classifier, International Journal of Electronics and Communication Engineering and Technology, 8(1), 2017, pp [21] Dr. Vangala Padmaja, A Brief Review on Hand Written Character Recognition, International Journal of Advanced Research in Engineering and Technology (IJARET), 5(2), 2014, pp [22] A New Approach for Object Feature Extract and Recognition, 9 th International Conference on Advanced Computer Systems editor@iaeme.com