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Handwritten Signature Verification. Dhawan, Ashish Ganesan, Aditi R. ECE 533 Project – Fall 2005. Introduction. Need for signature verification: Signature: very common metric. Types of verification: Online - captures dynamic data. Offline - uses features from the image.

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Handwritten signature verification l.jpg

Handwritten Signature Verification

Dhawan, Ashish

Ganesan, Aditi R.

ECE 533 Project – Fall 2005


Introduction l.jpg
Introduction

  • Need for signature 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.



Pre processing l.jpg
Pre-processing

  • Noise Removal:

    • Gaussian Noise.

    • Use of Average filter.

  • Inversion of Image.

  • Conversion of Image to Binary:

    • Use of Automatic Global thresholding.


Slide5 l.jpg

Averaged and Inverted Image

Original Image

Thresholded Image


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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


Features extraction l.jpg
Features Extraction

  • Aspect ratio:

    • 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


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Features Extraction

  • Slope of the line joining the Centers of Gravity of the two halves of signature image.

Right Half

Left Half


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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.


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Conclusion and Future Work

  • Conclusion:

    • 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.


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