Offline signature verification and identification by hybrid features and Support Vector Machine

2011 ◽  
Vol 2 (4) ◽  
pp. 302 ◽  
Author(s):  
Bailing Zhang
Keyword(s):  
Support Vector Machine ◽  
Signature Verification ◽  
Support Vector ◽  
Hybrid Features ◽  
Offline Signature Verification

Smart Affect Recognition System for Real-Time Biometric Surveillance Using Hybrid Features and Multilayered Binary Structured Support Vector Machine

2020 ◽  
Author(s):  
Thamba Meshach W ◽  
Hemajothi S ◽  
Mary Anita E A
Keyword(s):  
Support Vector Machine ◽  
Confusion Matrix ◽  
Recognition System ◽  
Learning Technologies ◽  
Support Vector ◽  
Affect Recognition ◽  
Svm Classifier ◽  
Ecg Signal ◽  
Hybrid Features ◽  
Structured Support Vector Machine

Abstract Human affect recognition (HAR) using images of facial expression and electrocardiogram (ECG) signal plays an important role in predicting human intention. This system improves the performance of the system in applications like the security system, learning technologies and health care systems. The primary goal of our work is to recognize individual affect states automatically using the multilayered binary structured support vector machine (MBSVM), which efficiently classify the input into one of the four affect classes, relax, happy, sad and angry. The classification is performed efficiently by designing an efficient support vector machine (SVM) classifier in multilayer mode operation. The classifier is trained using the 8-fold cross-validation method, which improves the learning of the classifier, thus increasing its efficiency. The classification and recognition accuracy is enhanced and also overcomes the drawback of ‘facial mimicry’ by using hybrid features that are extracted from both facial images (visual elements) and physiological signal ECG (signal features). The reliability of the input database is improved by acquiring the face images and ECG signals experimentally and by inducing emotions through image stimuli. The performance of the affect recognition system is evaluated using the confusion matrix, obtaining the classification accuracy of 96.88%.

scholarly journals Writer Independent Manipuri Offline Signature Verification using Transfer Learning Technique of Convolutional Neural Network

2019 ◽  
Vol 9 (1S) ◽  
pp. 76-80
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Learning Ability ◽  
Signature Verification ◽  
Support Vector ◽  
Svm Classifier ◽  
Powerful Learning ◽  
Offline Signature Verification ◽  
Learning Technique ◽  
Handwritten Signature

Automatic Signature Verification system is used to verify whether a signature is genuine or forged. Forged Signatures are those signatures that a person produced by imitating the signature of another person. Automatic Signature Verification is very important as a person’s handwritten signature is used everywhere to authenticate themselves and there is not very much difference between a genuine signature and the imitation of it, i.e. a forged signature. In this work, signature verification is done using different pre-trained Convolutional Neural Networks (CNNs). Convolutional Neural Network has powerful learning ability, and it can be used to distinguish between a genuine and a forged signature automatically. In this experiment, Manipuri signature dataset was used, the dataset was prepared originally and it contains 729 genuine signatures and 243 forged signatures. Features were extracted from pre-trained networks and classification was done using binary Support Vector Machine (SVM) classifier and the performances of the networks were compared. And according to the experiment we achieved a classification accuracy of 84.7 using VGG19 features, accuracy of 86.8 using VGG16 features and accuracy of 81.9 using Alexnet features.

scholarly journals An Improved Geo-Textural Based Feature Extraction Vector For Offline Signature Verification

2019 ◽  
pp. 1-14 ◽  
Author(s):  
Kennedy Gyimah ◽  
Justice Kwame Appati ◽  
Kwaku Darkwah ◽  
Kwabena Ansah
Keyword(s):  
Feature Extraction ◽  
Feature Vector ◽  
Least Square Method ◽  
Kernel Functions ◽  
Least Square ◽  
Signature Verification ◽  
Support Vector ◽  
Offline Signature Verification ◽  
Occurrence Matrix ◽  
Handwritten Signature

In the field of pattern recognition, automatic handwritten signature verification is of the essence. The uniqueness of each person’s signature makes it a preferred choice of human biometrics. However, the unavoidable side-effect is that they can be misused to feign data authenticity. In this paper, we present an improved feature extraction vector for offline signature verification system by combining features of grey level occurrence matrix (GLCM) and properties of image regions. In evaluating the performance of the proposed scheme, the resultant feature vector is tested on a support vector machine (SVM) with varying kernel functions. However, to keep the parameters of the kernel functions optimized, the sequential minimal optimization (SMO) and the least square method was used. Results of the study explained that the radial basis function (RBF) coupled with SMO best support the improved featured vector proposed.

scholarly journals Signature Verification Using Support Vector Machine and Convolution Neural Network

2021 ◽  
Vol 12 (1S) ◽  
pp. 80-89
Author(s):  
Kritika Vohra, Et. al.
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Feature Extraction ◽  
Aspect Ratio ◽  
Convex Hull ◽  
Unique Feature ◽  
Signature Verification ◽  
Support Vector ◽  
Individual Signature ◽  
Business Functions

Signature is used for recognition of an individual. Signature is considered as a mark that an individual write on a paper for his/her identity or proof. It is used as a unique feature for identifying an individual. It is highly used in social and business functions which gives rise to verification of signature. There are chances of signature getting forged. Hence, the need to identify signature as genuine of forged is utmost important. In this paper, identification of signature as genuine or forged is done using two approaches. First approach is using SVM and second is using CNN. For SVM, pre-processing of signature image is done and feature extraction is performed. Features extracted are histogram of gradient, shape, aspect ratio, bounding area, contour area and convex hull area. Further, SVM is applied to classify signature as genuine or forged and accuracy is determined. In the second approach, signature image is pre-processed, CNN is used to classify signature as genuine or forged and accuracy is determined. Dataset used here is ICDAR Dutch dataset along with 80 signatures taken from 4 people.Dutch dataset consists of 362 signature imagesand signature images taken from 4 people consists 10 genuine and 10 forged signatures which sums to 442 signature images. The proposed system provides accuracy of 86.39% using SVM and around 83.78% using CNN.

scholarly journals A Comparative Study of Transfer Learning Models for Offline Signature Verification and Forgery Detection

2021 ◽  
Vol 23 (07) ◽  
pp. 1129-1139
Author(s):  
Manikantha K ◽  
◽  
Aishwarya R Bhat ◽  
Pavani Nerella ◽  
Pooja Baburaj ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Deep Learning ◽  
Comparative Study ◽  
Signature Verification ◽  
Support Vector ◽  
Learning Models ◽  
Forgery Detection ◽  
Offline Signature Verification ◽  
Nearest Neighbours ◽  
Distinct Features

Recognising one’s identity to enter a system is called authentication. This process can take various forms where users input the system with a set of identifying credentials to access the system. Signatures belong to behavioural biometric, where the distinct features of every individual are considered in order to corroborate the person’s identity. The act of falsely imitating one’s signature biometric to impersonate and leverage access to their asset is called signature forgery. Our paper presents a comparative study of various deep learning models using Siamese architecture, over a wide catalogue of signature images. Openly available datasets like CEDAR, Handwritten Signatures dataset from Kaggle, ICDAR 2011 SigComp, and BH-Sig260 signature corpus are used to train the models. A set of classifiers – Support Vector Classifiers (SVC), Gaussian Naïve Bayes (GNB), Logistic Regression (LR) and K-Nearest Neighbours (KNN) are applied sequentially to classify the signature as genuine or forged.

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