Algorithm 1.
Region Growing Segmentation
Algorithm 2.
Iterative Closest Point Registration
Figure 1.
Results of preprocessing steps of genuine signatures from first author
Figure 2.
Results of preprocessing steps of genuine signatures from second author
Figure 3.
Registered signatures
Figure 4.
Part of registered Chinese signatures.
Figure 5.
Part of registered Chinese signatures.
Table 1
Summary of results for the twelve constraints and three databases
| CHINESE | |||
|---|---|---|---|
| Constraint | Best accuracy (k) | FAR/FRR (k) | Accuracy |
| SQmin < k · SRmin | 72.48% (2.2995) | 42.23% / 42.50% (1.175) | 57.70% |
| SQavg < k · SRmin | 64.07% (2.195) | 39.51% / 39.17% (1.745) | 60.57% |
| SQmax < k · SRmin | 66.53% (2.995) | 38.69% / 39.17% (2.430) | 61.19% |
| SQmin < k · SRavg | 78.44% (0.700) | 40.05% / 40.83% (0.530) | 59.75% |
| SQavg < k · SRavg | 83.37% (0960) | 29.97% / 29.17% (0.815) | 70.23% |
| SQmax < k · SRavg | 81.52% (1.420) | 24.25% / 24.17% (1.160) | 75.77% |
| SQmin < k · SRmax | 77.41% (0.495) | 38.69% / 38.33% (0.330) | 61.40% |
| SQavg < k · SRmax | 76.59% (0.685) | 31.06% / 29.17% (0.500) | 69.40% |
| SQmax < k · SRmax | 79.06% (0.880) | 27.25% / 25.83% (0.705) | 73.10% |
| SQmin < k | 77.41% (0.090) | 37.87% / 46.67% (0.065) | 59.96% |
| SQavg < k | 78.44% (0.140) | 28.88% / 28.33% (0.100) | 71.25% |
| SQmax < k | 80.90% (0.170) | 25.34% / 24.17% (0.140) | 74.95% |
| DUTCH | |||
| Constraint | Best accuracy (k) | FAR/FRR (k) | Accuracy |
| SQmin < k · SRmin | 69.31% (0.975) | 31.92% / 32.41% (1.040) | 67.83% |
| SQavg < k · SRmin | 73.50% (1.325) | 29.89% / 30.56% (1.435) | 69.77% |
| SQmax < k · SRmin | 71.41% (1.580) | 31.14% / 31.33% (1.900) | 68.76% |
| SQmin < k · SRavg | 67.52% (0.600) | 32.71% / 32.56% (0.605) | 67.37% |
| SQavg < k · SRavg | 79.49% (0.845) | 20.81% / 20.83% (0.860) | 79.18% |
| SQmax < k · SRavg | 78.24% (1.120) | 22.54% / 22.69% (1.145) | 77.39% |
| SQmin < k · SRmax | 61.93% (0.370) | 38.34% / 37.81% (0.370) | 61.93% |
| SQavg < k · SRmax | 71.25% (0.535) | 29.89% / 28.70% (0.515) | 70.71% |
| SQmax < k · SRmax | 74.83% (0.715) | 25.04% / 25.77% (0.690) | 74.59% |
| SQmin < k | 61.62% (0.090) | 42.10% / 39.66% (0.075) | 59.13% |
| SQavg < k | 69.77% (0.110) | 31.46% / 31.17% (0.105) | 68.69% |
| SQmax < k | 73.66% (0.140) | 27.54% / 25.15% (0.140) | 73.66% |
| JAPANESE | |||
| Constraint | Best accuracy (k) | FAR/FRR (k) | Accuracy |
| SQmin < k · SRmin | 63.95% (1.025) | 36.11% / 36.63% (0.990) | 63.65% |
| SQavg < k · SRmin | 66.52% (1.345) | 36.61% / 33.83% (1.400) | 66.29% |
| SQmax < k · SRmin | 67.57% (1.640) | 32.92% / 33.17% (1.840) | 66.97% |
| SQmin < k · SRavg | 68.02% (0.610) | 35.56% / 36.30% (0.545) | 64.10% |
| SQavg < k · SRavg | 74.51% (0.830) | 28.47% / 28.38% (0.765) | 71.57% |
| SQmax < k · SRavg | 76.55% (1.090) | 25.83% / 25.41% (1.020) | 74.36% |
| SQmin < k · SRmax | 67.35% (0.395) | 34.72% / 36.47% (0.340) | 64.48% |
| SQavg < k · SRmax | 74.06% (0.545) | 29.31% / 29.87% (0.465) | 70.44% |
| SQmax < k · SRmax | 76.92% (0.700) | 25.28% / 26.07% (0.625) | 74.36% |
| SQmin < k | 66.52% (0.125) | 36.39% / 33.33% (0.110) | 65.01% |
| SQavg < k | 72.47% (0.160) | 29.72% / 28.38% (1.155) | 70.89% |
| SQmax < k | 74.36% (0.225) | 25.56% / 25.91% (0.210) | 74.28% |
Figure 6.
False Acceptance Rate (FAR) and False Rejection Rate (FRR) respect to the thresholding parameter k and SQavg < k · SRavg (Dutch dataset).
Figure 7.
False Acceptance Rate (FAR) and False Rejection Rate (FRR) respect to the thresholding parameter k and SQmax < k · SRavg (Chinese dataset).
Figure 8.
False Acceptance Rate (FAR) and False Rejection Rate (FRR) respect to the thresholding parameter k and SQmax < k · SRavg (Japanese dataset).
Table 2
Comparison of our results with those got from the systems submitted to SigComp2011 for the Dutch dataset
| Author | Accuracy (%) | FRR | FAR |
|---|---|---|---|
| Qatar | 97.67 | 2.47 | 2.19 |
| Qatar | 95.57 | 4.48 | 4.38 |
| HDU | 87.80 | 12.35 | 12.05 |
| Sibanci University | 82.91 | 17.93 | 16.41 |
| Proposed | 79.25 | 20.81 | 20.83 |
| Anonymous-1 | 77.89 | 22.22 | 21.75 |
| DFKI | 75.84 | 23.77 | 24.57 |
| Anonymous | 71.02 | 29.17 | 28.79 |
Table 3
Comparison of our results with those got from the systems submitted to SigComp2011 for the Chinese dataset.
| Author | Accuracy (%) | FRR | FAR |
|---|---|---|---|
| Sibanci | 80.04 | 21.01 | 19.62 |
| Proposed | 75.77 | 24.25 | 24.17 |
| Anonymous1 | 73.10 | 27.50 | 26.70 |
| HDU | 72.90 | 27.50 | 26.98 |
| DFKI | 62.01 | 37.50 | 38.15 |
| Anonymous2 | 61.81 | 38.33 | 38.15 |
| Qatar (Chinese opt) | 56.06 | 45.00 | 43.60 |
| Qatar (Dutch opt) | 51.95 | 50.00 | 47.41 |
Table 4
Comparison of our results with those got from the systems submitted to SigWiComp2013 for the Japanese dataset.
| Author | Accuracy (%) | FRR | FAR |
|---|---|---|---|
| Sab1 | 90.72 | 9.74 | 9.72 |
| Sab2 | 89.82 | 10.23 | 10.14 |
| Sab3 | 86.95 | 13.04 | 13.06 |
| Teb1 | 76.70 | 23.60 | 23.06 |
| Proposed | 76.55 | 25.83 | 25.41 |
| Teb5 | 74.59 | 25.41 | 25.42 |
| Teb2 | 73.98 | 26.07 | 25.97 |
| Bud | 72.70 | 25.23 | 25.36 |
| Teb4 | 72.10 | 25.89 | 27.92 |
| Teb3 | 68.33 | 31.35 | 31.94 |
| Qatar | 66.67 | 33.33 | 33.33 |