Chord oriented gap feature for offline signature verification

In this paper, we address offline signature verification by proposing a new Partial Invariant Chord Oriented Gap (PICOG) feature. The new heuristically developed feature is conceptualized after observing the directional variation of the gaps (sequence of white pixels) between signature strokes. A set of unique and partial invariant chords is identified using genuine and forgery training signatures in the writer dependent system. Different sets of PICOG chords are selected for each writer by defining a threshold (d_jnv). The similarity threshold (d_th) is computed by performing another training step using the PICOG chords. A majority score based approach is selected to determine if the testing signature is genuine or forgery. A maximum accuracy of 82.27% is obtained on the widely used and publicly available, noisy signature database (CEDAR).