Fast recognition and verification of 3D air signatures using convex hulls. (15th June 2018)

• Record Type: Journal Article
• Authors: Behera, Santosh Kumar; Dogra, Debi Prosad; Roy, Partha Pratim
• Is Part Of: Expert systems with applications. Volume 100(2018)
• Page Start: 106
• Page End: 119
• Publication Date: 2018-06-15
• Subjects: Signature recognition -- Signature verification -- 3D sequence analysis -- Convex hull; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2018.01.042
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: An approach for Fast 3D Air Signature recognition and verification. An user created dataset is being used. Leap motion sensor is used for data acquisition. Convex Hull points have been used for feature vector. Recognition and verification time is drastically reduced. Abstract: 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.