1. Handwritten Signature Verification
Dhawan, Ashish
Ganesan, Aditi R.
ECE 533 Project – Fall 2005

2. Introduction Need for signature verification: Types of verification:
Signature: very common metric.
Types of verification:
Online - captures dynamic data.
Offline - uses features from the image.
Tough pattern recognition problem.
Types of forgeries:
Casual.
Skilled.

3. Approach

4. Pre-processing Noise Removal: Inversion of Image.
Gaussian Noise.
Use of Average filter.
Inversion of Image.
Conversion of Image to Binary:
Use of Automatic Global thresholding.

5. Averaged and Inverted Image
Original Image
Thresholded Image

6. Geometric Features Extraction
Slant Angle:
Signature is assumed to rest on an imaginary line known as the Baseline.
The angle of inclination of the baseline to the horizontal is called the Slant Angle.
Center of Gravity.
Original Image
Baseline Rotated Image

7. Features Extraction Aspect ratio: Normalized Area:
Ratio of width to height of the signature.
Normalized Area:
Ratio of the area occupied by signature pixels to the area of the bounding box.
Bounding box of the signature

8. Features Extraction
Slope of the line joining the Centers of Gravity of the two halves of signature image.
Right Half
Left Half

9. Verification and Results
Extracted features from Test-Images are used in deriving the mean values and standard deviations, which are used for final verification.
The Euclidian distance in the feature space measures the proximity of a query signature image to the genuine signature image of the claimed person.
If this distance is below a certain threshold then the query signature is verified to be that of the claimed person otherwise it is detected as a forged one.
Nature of Signature
No. of Samples
False Acceptance Rate
False Rejection Rate
Original 45
---
6.67%
Forged 30
10%

10. Conclusion and Future Work
The system is robust and can detect random, simple and semi-skilled forgeries.
A larger database can reduce false acceptances as well as false rejections.
Future Work:
Collection of larger database.
Addition of extra features.
Number of edge points: Edge point is a point that has only one 8-neighbor.
Number of cross points. Cross point is a point that has at least three 8-neighbors.