Adaptive SIFT/SURF Algorithm for Off-line signature Recognition

Signature recognition is the process of verifying a writer’s identity by checking the signature against samples previously stored in the database. Several techniques such as the distance-based and statistical classifiers used for feature extraction on a signature image are not invariant to scaling and rotation and the Scale invariant feature transform (SIFT) though invariant to scaling and rotation cannot cater for intra-class variation (Transposition) among set of genuine signature images. This paper proposed a SIFT-SURF algorithm which is used for enhanced offline signature recognition. The SIFT-SURF algorithm computes integral image; obtains hessian data and interest point for each computed integral image; applied neuro-scaling PCA based radial basis function neural network to compute the optimal features for each signature image to come up with an algorithm that is invariant to scaling and rotation as well as reliably match transposition among genuine samples of a signature image. It was experimentally found that the newly developed adaptive SIFT-SURF algorithm performed better in times of computational time, scaling, rotation and transposition as compared to the existing SIFT. KeywordsSIFT, HSV, ADAPTIVE SIFT-SURF, SIGNATURE, NEURAL NETWORK.