scholarly journals Online Handwritten Signature Verification Using Neural Network Classifier Based on Principal Component Analysis

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Vahab Iranmanesh ◽  
Sharifah Mumtazah Syed Ahmad ◽  
Wan Azizun Wan Adnan ◽  
Salman Yussof ◽  
Olasimbo Ayodeji Arigbabu ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Rejection Rate ◽  
Component Analysis ◽  
Error Rates ◽  
Signature Verification ◽  
Feature Selection Technique ◽  
Online Signature ◽  
Online Signature Verification ◽  
Handwritten Signature

One of the main difficulties in designing online signature verification (OSV) system is to find the most distinctive features with high discriminating capabilities for the verification, particularly, with regard to the high variability which is inherent in genuine handwritten signatures, coupled with the possibility of skilled forgeries having close resemblance to the original counterparts. In this paper, we proposed a systematic approach to online signature verification through the use of multilayer perceptron (MLP) on a subset of principal component analysis (PCA) features. The proposed approach illustrates a feature selection technique on the usually discarded information from PCA computation, which can be significant in attaining reduced error rates. The experiment is performed using 4000 signature samples from SIGMA database, which yielded a false acceptance rate (FAR) of 7.4% and a false rejection rate (FRR) of 6.4%.

OPTIMAL PROTOTYPE FUNCTIONS OF FEATURES FOR ONLINE SIGNATURE VERIFICATION

2004 ◽  
Vol 18 (07) ◽  
pp. 1189-1206 ◽  
Author(s):  
JULIO CESAR MARTÍNEZ ROMO ◽  
ROGELIO ALCÁNTARA SILVA
Keyword(s):  
Error Rates ◽  
Signature Verification ◽  
Static Characteristics ◽  
Continuous Space ◽  
Verification Algorithm ◽  
Verification Problem ◽  
Online Signature ◽  
Online Signature Verification ◽  
The One ◽  
Rejection Rates

It is well known that the approach of functions of time to represent the dynamic and static characteristics of signatures usually outperforms the approaches based on parameters; in addition to this result, we propose here that the model or prototype function of the discriminant features of the signatures should be created considering the signature verification problem as a bi-objective optimization problem in which the false acceptance and false rejection rates are minimized simultaneously; to accomplish these goals, a discrete space of solutions is searched by a genetic algorithm, and a continuous space of solutions is searched by a modified gradient method, both spaces containing candidate prototype functions and the one that best meets some optimization criteria is first chosen as the optimal prototype function and then improved. Given that creating the prototype functions of features is just one of the earlier steps of a signature verification system, we also propose here a scheme of signature verification algorithm with intelligent classification. Our approach was tested in the context of random and highly skilled forgeries, with error rates below 0.1% over 7,300 verifications. Our database consisted of 1,762 exemplars, containing genuine signatures and skilled forgeries from 36 persons. Comparison to other methods of making prototype functions of features is shown.

Principal component analysis for the early detection of mastitis and lameness in dairy cows

2013 ◽  
Vol 80 (3) ◽  
pp. 335-343 ◽  
Author(s):  
Bettina Miekley ◽  
Imke Traulsen ◽  
Joachim Krieter
Keyword(s):  
Principal Component Analysis ◽  
Early Detection ◽  
Principal Component ◽  
Component Analysis ◽  
Error Rates ◽  
Sensor Data ◽  
Sequence Length ◽  
Feeding Patterns ◽  
Original Dataset ◽  
Block Sensitivity

This investigation analysed the applicability of principal component analysis (PCA), a latent variable method, for the early detection of mastitis and lameness. Data used were recorded on the Karkendamm dairy research farm between August 2008 and December 2010. For mastitis and lameness detection, data of 338 and 315 cows in their first 200 d in milk were analysed, respectively. Mastitis as well as lameness were specified according to veterinary treatments. Diseases were defined as disease blocks. The different definitions used (two for mastitis, three for lameness) varied solely in the sequence length of the blocks. Only the days before the treatment were included in the blocks. Milk electrical conductivity, milk yield and feeding patterns (feed intake, number of feeding visits and time at the trough) were used for recognition of mastitis. Pedometer activity and feeding patterns were utilised for lameness detection. To develop and verify the PCA model, the mastitis and the lameness datasets were divided into training and test datasets. PCA extracted uncorrelated principle components (PC) by linear transformations of the raw data so that the first few PCs captured most of the variations in the original dataset. For process monitoring and disease detection, these resulting PCs were applied to the Hotelling's T2 chart and to the residual control chart. The results show that block sensitivity of mastitis detection ranged from 77·4 to 83·3%, whilst specificity was around 76·7%. The error rates were around 98·9%. For lameness detection, the block sensitivity ranged from 73·8 to 87·8% while the obtained specificities were between 54·8 and 61·9%. The error rates varied from 87·8 to 89·2%. In conclusion, PCA seems to be not yet transferable into practical usage. Results could probably be improved if different traits and more informative sensor data are included in the analysis.

scholarly journals A SIGNATURE IDENTIFICATION SYSTEM WITH PRINCIPAL COMPONENT ANALYSIS AND STENTIFORD THINNING ALGORITHMS

2015 ◽  
Vol 14 (9) ◽  
pp. 6074-6084 ◽  
Author(s):  
Olatubosun Olabode ◽  
AdeniyiJide Kehinde ◽  
Akinyede Olufemi ◽  
Oluwadare A. Samuel ◽  
Fasoranbaku A. Olusoga
Keyword(s):  
Principal Component Analysis ◽  
Research Work ◽  
Principal Component ◽  
Component Analysis ◽  
Features Extraction ◽  
Design System ◽  
Manhattan Distance ◽  
Identification System ◽  
Thinning Algorithm ◽  
Handwritten Signature

Several biometric security systems have been implemented. Biometric is the use of a person’s physiological or behavioural characteristics to identify the individual. An example of behavioural method of biometric is signature identification. Signature identification is the use of handwritten signature to identify a person. This paper attempt design and implement an algorithm for handwritten signature identification. The signature identification system consists of signature acquisition, preprocessing, features extraction and matching stages. Signature acquisition can be either online or offline (both were considered in this research work). Online signatures are obtained by signing on digital tablets while offline signatures are scanned (or snapped) into the system. Preprocessing stage of the system include turning the image to greyscale. The grey image is further converted to binary (black and white). The image is then thinned, using Stentiford thinning algorithm. Stentiford thinning algorithm in an iterative thinning method with a good thinned imaged output. The image is finally cropped to rid the image of unnecessary white spaces. For features extraction, principal component analysis is used. Principal Component Analysis is a good statistical tool for identifying pattern in data. Features extracted from each signature are stored as a template. After features extraction, the distance between signature templates are computed using Manhattan distance. If the distance exceeds a certain threshold, the test signature is rejected (otherwise it is accepted). The design system has a FAR of 4% and an FRR of 6% for offline signatures. A FAR of 2% and an FRR of 3% were obtained for online signatures

scholarly journals A comparative study on dimensionality reduction between principal component analysis and k-means clustering

2019 ◽  
Vol 16 (2) ◽  
pp. 752
Author(s):  
Norsyela Muhammad Noor Mathivanan ◽  
Nor Azura Md.Ghani ◽  
Roziah Mohd Janor
Keyword(s):  
Principal Component Analysis ◽  
Feature Selection ◽  
Time Complexity ◽  
Principal Component ◽  
Component Analysis ◽  
Classification Model ◽  
Small Data ◽  
Feature Selection Technique ◽  
Data Set ◽  
Selection Technique

<span>The curse of dimensionality and the empty space phenomenon emerged as a critical problem in text classification. One way of dealing with this problem is applying a feature selection technique before performing a classification model. This technique helps to reduce the time complexity and sometimes increase the classification accuracy. This study introduces a feature selection technique using K-Means clustering to overcome the weaknesses of traditional feature selection technique such as principal component analysis (PCA) that require a lot of time to transform all the inputs data. This proposed technique decides on features to retain based on the significance value of each feature in a cluster. This study found that k-means clustering helps to increase the efficiency of KNN model for a large data set while KNN model without feature selection technique is suitable for a small data set. A comparison between K-Means clustering and PCA as a feature selection technique shows that proposed technique is better than PCA especially in term of computation time. Hence, k-means clustering is found to be helpful in reducing the data dimensionality with less time complexity compared to PCA without affecting the accuracy of KNN model for a high frequency data.</span>

scholarly journals Intrusion Detection System using One Class SVM with and without Feature Selection in Wormhole Attack Detection

2019 ◽  
Vol 9 (2S4) ◽  
pp. 629-638
Keyword(s):  
Principal Component Analysis ◽  
Feature Selection ◽  
Ad Hoc ◽  
Detection System ◽  
Principal Component ◽  
Component Analysis ◽  
Svm Classifier ◽  
Feature Selection Technique ◽  
Selection Technique ◽  
Wormhole Attack

An Ad-hoc network is a kind of wireless construction from one to another computer, without having Wi-Fi access point or Router. However, the Ad hoc approach offers marginal security and decreases the data transfer rate. Consequently, it helps the attacker to connect with the ad-hoc network without any trouble. Therefore, a robust and reliable intrusion detection system (IDS) is a necessity of today’s information security domain. These IDS systems play a vital role in monitoring the threats encountered in a network by detecting the change in the normal profile due to attacks. Recently, to detect attacks the IDS are being equipped with machine learning algorithms to attain better accuracy and fast detection speed. Most of the IDS use different network features. However, enormous number of features makes the detection and prevention complicated. The IDS presented in this paper employs random forest and principal component analysis to minimize the number of features for network IDS for wireless ad hoc networks. The one class SVM has been used for detection of worm hole attack with and without feature selection. The performances of these approaches are compared with various existing techniques with false positive rate (FPR), accuracy and detection rate. Here, the accuracy improves and false positive rate reduces when intrusion is detected with feature selection technique. This paper discusses the performance of the one class SVM classifier in the wireless adhoc network IDS with random forest feature selection and principal component analysis feature selection techniques and one class SVM classifier without feature selection technique in the detection of wormhole attack. And the performance of one class SVM IDS is better in the detection of wormhole attack while it is implemented with principal component analysis feature selection technique.

scholarly journals Forgery Detection in Dynamic Signature Verification by Entailing Principal Component Analysis

2007 ◽  
Vol 2007 ◽  
pp. 1-8 ◽  
Author(s):  
Shohel Sayeed ◽  
S. Andrews ◽  
Rosli Besar ◽  
Loo Chu Kiong
Keyword(s):  
Principal Component Analysis ◽  
Detection System ◽  
Principal Component ◽  
Component Analysis ◽  
Error Rates ◽  
Forgery Detection ◽  
Data Glove ◽  
And Performance ◽  
Dynamic Signature ◽  
Operational Data

The critical analysis of the data glove-based signature identification and forgery detection system emphasizes the essentiality of noise-free signals for input. Lucid inputs are expected for the accuracy enhancement and performance. The raw signals that are captured using 14- and 5-electrode data gloves for this purpose have a noisy and voluminous nature. Reduction of electrodes may reduce the volume but it may also reduce the efficiency of the system. The principal component analysis (PCA) technique has been used for this purpose to condense the volume and enrich the operational data by noise reduction without affecting the efficiency. The advantage of increased discernment in between the original and forged signatures using 14-electrode glove over 5-electrode glove has been discussed here and proved by experiments with many subjects. Calculation of the sum of mean squares of Euclidean distance has been used to project the advantage of our proposed method. 3.1% and 7.5% of equal error rates for 14 and 5 channels further reiterate the effectiveness of this technique.

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