This study evaluates the consistency and reliability of signature verification across different devices, including iPads and digitizers, using an algorithm to analyze signature similarities. Results indicate that the algorithm is unreliable, as margins of error exceeded the acceptable threshold of 3%. Various comparisons showed significant rates of false positives and negatives, suggesting the need for more accurate signature matching tools.

1. SIGNATURE AUTHENTICATION CONSISTENCY ACROSS DEVICES
The purpose of this study is to determine the consistency of signatures and the reliability of verification across various devices. These devices include an
iPad (with finger and stylus) and digitizers (back-lit and non-back-lit) for the purpose of electronic consent. This study was conducted using two to four (varied
for each device and person) signatures per team member and then cross-comparing every combination of signature to determine the stability of using the
algorithm across various devices with a given threshold.
David Mihai, Jasmine Jones, Michael Brockly, Richard Guest, Stephen Elliott
Overview
Devices Used Matlab SOP
Results
73
87
18 12
0
20
40
60
80
100
120
NumberofComparisons
Results
38%
10%
46%
6%
Signature Results
(Uncorrected)
Positive
False
Positive
Negative
False
Negative
74 91
17 8
0
20
40
60
80
100
120
NumberofComparisons
Results
39%
9%
48%
4%
Signature Results
(Corrected for Image
Type)
Positive
False
Positive
Negative
False
Negative
From the results, and based off of the assumption that any margin of error greater than 3% was considered to be unsatisfactory, the devices used could not
reliably compare signatures using the algorithm. “Positive” means we matched a valid signature across devices. “False Positive” means that a signature was
recognized as valid when it was not. “Negative” means that a signature was recognized to be invalid at the proper time. “False Negative” is when a signature that
was valid was recognized as an invalid signature. This analysis includes results that were both corrected and uncorrected for image type and quality (such as
resolution or sharpness of image). This correction accounts for the differences between, in this particular experiment, lower-quality bitmap files and higher-quality
jpg images.
In order to conduct this experiment, a tool was needed to compare and
provide a quantitative value of signature similarity. In order to process this
data, an algorithm provided by Dr. Guest at the University of Kent was used.
The SURF standard signature algorithm returns a series of mean reference
point distances between greyscale images. In order to correct for outliers
and to have the most accurate results, only the nearest 50% of distances
were used in calculating the sum. For example, using this algorithm, calling:
>> SURFstaticcompare(‘image1.bmp’,’image2.bmp')
Using this algorithm in Matlab 7.8.0 will return –
ans =
sum1: 0.1363 (Returns 75% of points)
sum2: 0.1295 (Returns 66% of points)
sum3: 0.1198 (Returns 50% of points – value used as per Dr.
Guest’s recommendation)
sum4: 0.1084 (Returns 33% of points)
sum5: 0.1647 (Returns 100% of points)
The sum3 values were then analyzed and compared to a threshold to
determine a match or rejection dependent on the image type and quality.
iPad 2 ePad ink backlit
Signature Application on iPad Stylus used with iPad
ePad ink Non-Backlit