Recognizing Online Digital Signatures Using Kernel Based Classification Techniques
R. Ravi Chakravarthi, Anna Palagan C, Ravindra Ratilal Dharamshi, Anand J Dhas, Shrikant Taware, Ahilan Appathurai · Research Square · 2021
Abstract In recent education system, project submission is crucial for college students to complete their respective studies. The understudies needed to propose their undertaking before finishing the pre-last year. One of the critical assessment forms like course Project Registration System (PRS) helps the students and their education board to enhance the knowledge and skill level required for competitive world.PRS is the efficient methods to authenticate the proposal utilizing the online digital signature recognition criteria. During project submission, authentication is important to prevent the unauthorized submission of proposal and contrast the signature utilizing classification techniques such as Kernel Based Artificial Neural Network (K-ANN), Kernel Based K-Nearest Neighbor (K-KNN), Kernel Based Self Organizing Map (K-SOM) and Kernel based Support Vector Machine (K-SVM). The prime objectives of this research work was to recognize the genuine online digital signatures from the assortment of genuine, skilled, unskilled and random types of forged signatures by using the dynamic features like pen pressure, altitude, velocity, azimuth and duration taken by the legitimate person for signing using the proposed K-SVM classification techniques. The data collection based on online digital signature with various students and the proposed classification techniques gives better performance and accuracy compared with other techniques.