Table 7 CS-LBP feature-based results: trained with only genuine signatures.
From: A hybrid machine learning framework for offline signature verification using gray wolf optimization
\(\:\mathcal{H}\mathcal{M}\mathcal{L}\mathcal{F}\) | Avg \(\:\varvec{F}\varvec{A}\varvec{R}\) (%) | Avg \(\:\varvec{F}\varvec{R}\varvec{R}\) (%) | Avg \(\:\varvec{A}\varvec{E}\varvec{R}\) (%) | Avg Accuracy (%) |
|---|---|---|---|---|
RBF | 55.30 | 2.65 | 28.98 | 71.02 |
Linear | 71.97 | 0 | 35.98 | 64.02 |
Polynomial | 90.9 | 0 | 45.45 | 54.55 |
