Combination of Signature Verification Techniques by SVM

2012 ◽  
Author(s):  
Takashi Ito ◽  
Wataru Ohyama ◽  
Tetsushi Wakabayashi ◽  
Fumitaka Kimura
Keyword(s):  
Signature Verification ◽  
Verification Techniques

scholarly journals An Appraisal of Off-line Signature Verification Techniques

2015 ◽  
Vol 7 (4) ◽  
pp. 67-75 ◽  
Author(s):  
Abdul Salam Shah ◽  
◽  
M. N. A. Khan ◽  
Asadullah Shah
Keyword(s):  
Signature Verification ◽  
Verification Techniques

scholarly journals SIGNATURE IDENTIFICATION AND VERIFICATION SYSTEMS: A COMPARATIVE STUDY ON THE ONLINE AND OFFLINE TECHNIQUES

2020 ◽  
Vol 5 (1) ◽  
pp. 28-45
Author(s):  
Nehal Hamdy Al-banhawy ◽  
◽  
Heba Mohsen ◽  
Neveen Ghali ◽  
◽  
...  
Keyword(s):  
Feature Extraction ◽  
Comparative Study ◽  
Active Area ◽  
Signature Verification ◽  
Classification Techniques ◽  
Verification Systems ◽  
Identification Techniques ◽  
Handwritten Signature ◽  
Verification Techniques

Handwritten signature identification and verification has become an active area of research in recent years. Handwritten signature identification systems are used for identifying the user among all users enrolled in the system while handwritten signature verification systems are used for authenticating a user by comparing a specific signature with his signature that is stored in the system. This paper presents a review for commonly used methods for pre-processing, feature extraction and classification techniques in signature identification and verification systems, in addition to a comparison between the systems implemented in the literature for identification techniques and verification techniques in online and offline systems with taking into consideration the datasets used and results for each system

Smartphone-based Handwritten Signature Verification using Acoustic Signals

2021 ◽  
Vol 5 (ISS) ◽  
pp. 1-26
Author(s):  
Run Zhao ◽  
Professor Dong Wang ◽  
Qian Zhang ◽  
Xueyi Jin ◽  
Ke Liu
Keyword(s):  
Information Exchange ◽  
Characteristic Curve ◽  
Acoustic Signals ◽  
Signature Verification ◽  
Special Device ◽  
Dominant Hand ◽  
Siamese Network ◽  
On Line ◽  
Handwritten Signature ◽  
Verification Techniques

Handwritten signature verification techniques, which can facilitate user authentication and enable secure information exchange, are still important in property safety. However, on-line automatic handwritten signature verification usually requires dynamic handwritten patterns captured by a special device, such as a sensor-instrumented pen, a tablet or a smartwatch on the dominant hand. This paper presents SonarSign, an on-line handwritten signature verification system based on inaudible acoustic signals. The key insight is to use acoustic signals to capture the dynamic handwritten signature patterns for verification. Particularly, SonarSign exploits the built-in speakers and microphones of smartphones to transmit a specially designed training sequence and record the corresponding echo for channel impulse response (CIR) estimation, respectively. Based on the sensitivity of CIR to the tiny surrounding environment changes including handwritten signature actions, SonarSign designs an attentional multi-modal Siamese network for end-to-end signatures verification. First, multi-modal CIR streams are fused to extract representative signature pattern features from spatio-temporal dimensions. Then an attentional Siamese network is elaborated to verify whether the given two signatures are from the same signatory. Extensive experiments in real-world scenarios show that SonarSign can achieve accurate and robust signatures verification with an AUC (Area Under ROC (Receiver Operating Characteristic) Curve) of 98.02% and an EER (Equal Error Rate) of 5.79% for unseen users.

scholarly journals Offline Signature Verification using Locally Optimized Distance-based Classification

2013 ◽  
Vol 50 ◽  
Author(s):  
Yaseen Moolla ◽  
Serestina Viriri ◽  
Fulufhelo Nelwamondo ◽  
Jules-Raymond Tapamo
Keyword(s):  
Feature Extraction ◽  
Research Work ◽  
Directional Pattern ◽  
Signature Verification ◽  
Extraction Technique ◽  
Classification Techniques ◽  
Local Directional Pattern ◽  
Handwritten Signature ◽  
Verification Techniques ◽  
Feature Extraction Technique

Although handwritten signature verification has been extensively researched, it has not achieved an optimal classification accuracy rate. Therefore, efficient and accurate signature verification techniques are required since signatures are still widely used as a means of personal verification. This research work presents efficient distance-based classification techniques as an alternative to supervised learning classification techniques (SLTs). The Local Directional Pattern (LDP) feature extraction technique was used to analyze the effect of using several different distance-based classification techniques. The classification techniques tested, are the Euclidean, Manhattan, Fractional, weighted Euclidean, weighted Manhattan, weighted fractional distances and individually optimized resampling of feature vector sizes. The best accuracy, of 90.8%, was achieved through applying a combination of the weighted fractional distances and locally optimized resampling classification techniques to the Local Directional Pattern feature extraction. These results are compared with results from literature, where the same feature extraction technique was classified with SLTs. The distance-based classification was found to produce greater accuracy than the SLTs.

Biometrics-Signature Verification Techniques

2000 ◽  
Vol 41 (3-4) ◽  
pp. 63-66
Author(s):  
Sharon James ◽  
Subhadeep Pal
Keyword(s):  
Signature Verification ◽  
Verification Techniques

Improvement of Japanese Signature Verification by Combined Segmentation-verification Approach

2014 ◽  
Vol 134 (12) ◽  
pp. 1809-1816
Author(s):  
Yuta Kamihira ◽  
Wataru Ohyama ◽  
Tetsushi Wakabayashi ◽  
Fumitaka Kimura
Keyword(s):  
Signature Verification
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