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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

Handwritten Signature Verification

Dhawan, Ashish

Ganesan, Aditi R.

ECE 533 Project – Fall 2005

introduction
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
Pre-processing
  • Noise Removal:
    • Gaussian Noise.
    • Use of Average filter.
  • Inversion of Image.
  • Conversion of Image to Binary:
    • Use of Automatic Global thresholding.
slide5
Averaged and Inverted Image

Original Image

Thresholded Image

geometric features extraction
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
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

features extraction8
Features Extraction
  • Slope of the line joining the Centers of Gravity of the two halves of signature image.

Right Half

Left Half

verification and results
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.
conclusion and future work
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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