Signature Verification Using Wavelet Decomposition Technique

Signature

Signature has been a distinguishing feature for person identification through ages. Even today an increasing number of transactions, especially financial, are being authorized via signatures, hence methods of automatic signature verification must be developed if authenticity is to be verified on a regular basis. Approaches to signature verification fall into two categories according to the acquisition of the data: On-line and Off-line. On-line data records the motion of the stylus while the signature is produced, and includes location, and possibly velocity, acceleration and pen pressure, as functions of time.

Online systems use this information captured during acquisition. These dynamic characteristics are specific to each individual and sufficiently stable as well as repetitive. Off-line data is a 2-D image of the signature. Processing Off-line is complex due to the absence of stable dynamic characteristics. Difficulty also lies in the fact that it is hard to segment signature strokes due to highly stylish and unconventional writing styles. The non-repetitive nature of variation of the signatures, because of age, illness, geographic location and perhaps to some extent the emotional state of the person, accentuates the problem. All these coupled together cause large intra-personal variation.

A robust system has to be designed which should not only be able to consider these factors but also detect various types of forgeries. The system should neither be too sensitive nor too coarse. It should have an acceptable trade-off between a low False Acceptance Rate (FAR) and a low False Rejection Rate (FRR).

We approach the problem in two steps. Initially the scanned signature image is preprocessed to be suitable for extracting features. Then the preprocessed image is used to extract relevant geometric parameters that can distinguish signatures of different persons.

Implementation Results and Discussion

Sample Result 1 :

The Following Diagram shows the Signature Analysis screen and different steps involved in the process.

Signature Analysis Result1

Sample Result 2 :

Signature Search - Sample Result 1 :

Signature Search - Sample Result 2 :

The Step by Step Transformation of Signature.

The Input Signature

The Auto Correlation Signal of the Signature

The FFT Signal of the Signature

The Final Wavelet Transformed Signal of the Signature

Comparing a person's signature with other signature is a challenging task in a secured computing environments like banks. The signature verification is so complex because, a persons signature will definitely will not match with his own previous signature, if we do a pixel by pixel comparison. So the simple template matching methods will always fail in the case of signature verification. In this project we are going to uses wavelet decomposition techniques to correlate the signatures with one another, after wavelet transformation.

By doing wavelet transformation of the input signal (signature), we will have a time-frequency representation of the signal. By comparing the time-frequency representation of the signal with one another, we can fin the exact match of a signal or signature.

Conclusion

The proposed wavelet transformation based signature verification system has been implemented and tested successfully by using Matlab6.5 and visual c++ on Windows operating system.

The Performance of the proposed algorithm was tested against signature database gathered from Internet resources. The system was tested with different kinds and numbers of signatures and overall acceptable results were arrived. The arrived results were significant and comparable. The accuracy of retrieval obtained while testing it with the small database is promising.

The algorithm uses simple geometric features to characterize signatures that effectively serve to distinguish signatures of different persons. The system is robust and can detect random, simple and semi-skilled forgeries but the performance deteriorates in case of skilled forgeries. A larger database can reduce false acceptances as well as false rejections. Using a higher dimensional feature space and also incorporating dynamic information gathered during the time of signature can also improve the performance. The concepts of Wavelet transforms hold a lot of promise in building systems with high accuracy.