Signature Recognition Using Neural Networks and Rule Based Decision Systems CSC 8810 Computational Intelligence Instructor Dr. Yanqing Zhang Presented By Mateena Syeda Shilpa Panaganti

Outline Introduction Signature Recognition System Design/Architecture Implementation Experimental Results Conclusions and Future Work

Introduction Authentication and Identification bears an important role in many fields Different types of authentication  Password  Picture recognition  Signature recognition  Finger print recognition Growing interest towards Biometric Authentication  Airports  Credit card validation  Surveillance

A Signature is defined as the name of a person written with his/her own hand Signature Recognition is the process of verifying the user signature with the ones stored in database A computationally expensive and a difficult process Helps in authenticating  Bank Checks  Credit Card Transactions  Property Documentation  Forensic Investigation Signature Recognition

 Line crossing Segmentation  Behavioral Analysis  Statistical Approach  Neural Networks Types of Signature Recognition

Proposed System Design Data acquisition – Getting input signatures Preprocessing – Removing erroneous data Feature Extraction – Recording of special characteristics Neural Network structure – Associating with each characteristic Training Networks - Organizing of the authentic and forged data Rule Based Decision Making – Establishing error rate, thresholds Performance evaluation – TAR, TRR, FAR, FRR

Neural Networks Rule Based Decision system Stroke Angles Moment Values Protein sequence Pre- Processing Sum of Grey Values System Architecture Result : Genuine/ Forged Input Signature Median Filtering Resizing

Implementation Data Acquisition Signatures Acquisition  A finite number of samples collected from few Subjects  Authentic  Forged  Each signature is scanned using a Scanner  Stored as image in TIF format

Preprocessing  MATLAB - Image Processing Toolbox  Normalized each signature image to fit a 128x256 vector  The Median Filter Removal of Noise/Spurious pixels Define Neighborhood size Choose the median intensity value among the pixels in the neighborhood Replace the pixel's intensity by the median value

Original Signature Preprocessed Signature

Feature Extraction  A Feature is any extractable measurement taken on the input pattern that is to be classified  The key is to choose and extract features that are  computationally feasible  lead to a good classification system with few misclassification errors  reduce the input data into a manageable amount of information without discarding valuable or vital information

 Moments  Moments are defined as  Moments are used to determine properties of a component  Also known as “invariant” - denotes an image or a shape feature which remains unchanged if that image or shape undergoes one or combination of the following changes:  Change of size (scale)  Change of position (translation)  Change of orientation (rotation)  Reflection

Result of Moment Features extracted from the Signature

Strokes  A global feature - important for distinguishing signatures  Developed a new method to determine Orientation of the signature at different points  Orientation is defined as inverse tangent of the Yn/Xn Orientation = Tan -1 (Yn / Xn )  The points at which the orientation is calculated are known as critical points  Established an Angle threshold θ  Stroke = TRUE, if Orientation > θ  3 features - X coordinate, Y coordinate and the angle at that position

Result of Stroke Features extracted from the Signature

Multi Neural Network Neural Network Training  Back propagation algorithm  Five different Neural Networks  Why five systems?  Each system is trained with three features Neural Network Testing  Five different testing results Decision Rules

Neural Networks Rule Based Decision system Stroke Angles Moment Values Protein sequence Preprocessed Input Signature Sum of Grey Values System Architecture Result : Genuine/ Forged

Decision Function The output from each network is a value between 0 and 1 indicating the degree of confidence in the genuineness of the presented signature A Threshold value between 0 and 1 is selected to authenticate the signature A signature is accepted if the output of the network exceeds the Threshold value The selection of Threshold value plays a vital role

Threshold Values Generally 0.5 is taken as Threshold value for Single Neural Network Here each Feature extracted is given an individual Threshold value Two Neural Network Systems resulting the testing results of Moments have 0.8 Threshold value. Two systems with stroke angles have 0.6 Threshold. One system resulting the sum of grey values has 0.7 Threshold value.

Decision Rules Two Moments and two stroke angles are considered as single element. Eight rules are drawn with respect to Decision function values of features. In Rules given below  M-Moments  A-Stroke Angles  S-Sum of Grey values

Decision Rules (cont..) If M>0.8 and A>0.6 and S>0.7 then accepted If M>0.8 and A>0.6 and S<0.7 then accepted If M>0.8 and S>0.7 and A<0.7 then accepted If S>0.7 and A>0.6 and M<0.8 then accepted If M>0.8 and A<0.6 and S<0.7 then rejected If S>0.7 and A<0.6 and M<0.8 then rejected If A>0.6 and M<0.8 and S<0.7 then rejected If M<0.8 and A<0.6 and S<0.7 then rejected

Performance Evaluation The performance of system is evaluated by four different categories TAR – True Acceptance Rate TRR – True Rejection Rate FAR – False Acceptance Rate FRR – False Rejection Rate The Main aim here is to increase the TAR and FRR, while decreasing TRR and FAR.

Results Signatures Acquisition  Each signature is scanned using a Scanner (Hewlett Packard Scan Jet) connected to Pentium 4 PC  Total 20 subjects are tested  For each subject 5 genuine signatures are collected and trained the system  8 are tested with forgeries  12 are authentic signature  The Neural Network system is trained with different values of parameters  The system is trained with various values of hidden neurons and error.

Multi-Network TAR Table TAR Moment1 Moment2 Stroke1 Stroke2 Grey Value Signature Signature Signature Signature Signature Signature Signature Signature Signature Signature Signature

TRR and FAR Tables TRRMoment1 Moment2 Stroke1 Stroke2 Grey Value Signature FAR Moment1 Moment2 Stroke1 Stroke2 Grey Value Signature

FRR Table FRR Moment1 Moment2 Stroke1 Stroke2 Grey Value Signature Signature Signature Signature Signature Signature Signature

Single Network TAR Output Signature Signature Signature Signature40.56 Signature Signature Signature Signature Signature TRROutput Signature Signature Signature FRR Output Signature Signature Signature Signature Signature FAROutput Signature Signature Signature

Conclusions Built a robust algorithm Consumes less time for execution Multi Network improved results Median Filtering preserved the sharpness of the signature Strokes evaluation resulted in more accuracy in detection Thresholds provided flexibility in controlling error in making a decision Reduced/eliminated fraud to a large extent

Future Work This system can be extended to Online Signature Recognition Enhanced Back-Propagation Algorithm and Batch Back- Propagation Algorithm can be implemented together for improved performance. New feature extractions can be added  Intensity/ Speed  Existence of breaks in signature

References 1. Anil K Jain -Online Signature Verification 2. SHENG-FUU LIN, YU-WEI -A Study on Chinese Carbon- Signature Recognition 3. The biometric resource center An Application of Biometric Technology: Signature Recognition Pattern recognition Median Filtering -

Thank You!