Fast recognition and verification of 3D air signatures using convex hulls

Recognition of signature is a method of identification, whereas verification takes the decision about its genuineness. Though recognition and verification both play important role in forensic sciences, however, recognition is of special importance to the banking sectors. In this paper, we present a methodology to analyse 3D signatures captured using Leap motion sensor with the help of a new feature-set extracted using convex hull vertices enclosing the signature. We have used k-NN and HMM classifiers to classify signatures. Experiments carried out using our dataset as well as publicly available datasets reveal that the proposed feature-set can reduce the computational burden significantly as compared to existing features. It has been observed that a 10-fold computational gain can be achieved with non-noticeable loss in performance using the proposed feature-set as compared with the existing high-level features due to significant reduction in the feature vector size. On a large dataset of 1600 samples, two of the existing features take approximately 60s and 3s to recognise signatures using k-NN and HMM classifiers. However, features constructed using convex hull vertices take 1.9s and 0.4s, respectively. Our proposed system can be used in applications where recognition and verification need to be performed quickly on large datasets comprising with billions of samples.