Abstract: The systems and methods relate to electronic signature verification based on topological stochastic models (TSM). The TSM may be trained on samples of known authentic signatures of a signee. Training the TSM may include TSM features extraction on the training samples to extract feature vectors, TSM features aggregation to aggregate the feature vectors, and optimal threshold estimation to determine an optimal threshold value. The optimal threshold value and overall aggregate of feature vectors may be used to evaluate feature vectors extracted from a signature to be verified. For example, a distance between the resulting feature vector extracted from the input sequence and the aggregated feature vector is determined. The distance is compared to the optimal threshold value to determine whether the signature in the input image is verified. The signature in the input image is verified if the distance is less than or equal to the optimal threshold value.

Abstract: The systems and methods relate to electronic signature verification based on topological stochastic models (TSM). The TSM may be trained on samples of known authentic signatures of a signee. Training the TSM may include TSM features extraction on the training samples to extract feature vectors, TSM features aggregation to aggregate the feature vectors, and optimal threshold estimation to determine an optimal threshold value. The optimal threshold value and overall aggregate of feature vectors may be used to evaluate feature vectors extracted from a signature to be verified. For example, a distance between the resulting feature vector extracted from the input sequence and the aggregated feature vector is determined. The distance is compared to the optimal threshold value to determine whether the signature in the input image is verified. The signature in the input image is verified if the distance is less than or equal to the optimal threshold value.