Automatic Signature Recognition and Verification Using Principal Components Analysis

Automatic Signature Verification: The State of the Art 1989-1993,R Plamondon & F Leclerc) An Evaluation of Parallel Strategies for Feature Vector Construction in Automatic Signature Verification System, M C Fairhurst and P Britton A Static Signature Verification System Based on a Cooperating Neural Networks Architecture, H Cardot et al Off-Line Verification of Japanese Signatures After Elimination of Background Patterns, I Yoshimura & M Yoshimura Structural Interpretation of Handwritten Signature Images, R Sabourin et al. Statistical Methods for On-Line Signature Verification , W Nelson et al. Component-Oriented Algorithms for Signature Verification, G Dimauro et al. The Design of an On-Line Signature Verification System: From Theory to Practice, R Plamondon. (Part contents).

Human perception of sign language can serve as inspiration for the improvement of automatic recognition systems. Experiments with human signers show that sign language signs contain redundancy over time. In this article, experiments are conducted to investigate whether comparable redundancies also exist for an automatic sign language recognition system. Such redundancies could be exploited, for example, by reserving more processing resources for the more informative phases of a sign, or by discarding uninformative phases. In the experiments, an automatic system is trained and tested on isolated fragments of sign language signs. The stimuli used were similar to those of the human signer experiments, allowing us to compare the results. The experiments show that redundancy over time exists for the automatic recognizer. The central phase of a sign is the most informative phase, and the first half of a sign is sufficient to achieve a recognition performance similar to that of the entire sign. These findings concur with the results of the human signer studies. However, there are differences as well, most notably the fact that human signers score better on the early phases of a sign than the automatic system. The results can be used to improve the automatic recognizer, by using only the most informative phases of a sign as input.

Biometric technologies, such as handwritten signature verification, are extremely useful for identifying individuals inside an organization or finance department. The improvement of picture categorization using deep learning (DL) neural networks has offered an opportunity to exhibit computer vision in contemporary research applications by applying image processing approaches. Manual signature verification is inefficient, error-prone, time-consuming, and inconvenient; therefore, it is critical to create an automatic signature verification recognition system. This research offers an automatic recognition method based on DL that makes use of the Grupo de Procesado Digital de Seales. The biggest publicly accessible handwritten signature dataset, the synthetic signature dataset, was used to classify the signatures of 100 people, each of whom possessed 24 genuine signatures and 30 forged signatures. An inception V3 transfer learning (TL) model is proposed by hyper-tuning different layers from the middle of its architecture and this model is fine-tuned by adding layers, such as flatten, dense (1024), dropout (0.5), and dense (1). The suggested model was tested against six well-known pre-trained TL convolutional neural network models: VGG 16, VGG 19, ResNet 50, ResNet 101, MobileNet, and EfficientNet. The suggested model surpasses the pre-trained models. Precision, sensitivity, and F1-score are likewise outperformed by the model, with the values of 88%, 88%, and 87%, respectively. The accuracy of the pre-trained models was evaluated as 80%, 81%, 77%, 73%, 71%, and 74%, respectively. The suggested fine-tuned inception V3 gives the highest accurate classifications, distinguishing between genuine and forged signatures with an accuracy of 88%. This study will aid researchers in developing more effective CNN-based models for offline signature verification with application to computer vision.

In this paper, a new face recognition method based on PCA (principal Component Analysis), LDA (Linear Discriminant Analysis) and neural networks is proposed. This method consists of four steps: i) Preprocessing, ii) Dimension reduction using PCA, iii) feature extraction using LDA and iv) classification using neural network. Combination of PCA and LDA is used for improving the capability of LDA when a few samples of images are available and neural network classifier is used to reduce number misclassification caused by not-linearly separable classes. The proposed method was tested on Yale face database. Experimental results on this database demonstrated the effectiveness of the proposed method for face recognition with less misclassification in comparison with previous methods.

Image analytics solutions are very crucial for not only security, but also for authentication purposes. One such intriguing new Deep Learning application is automatic signature verification. Signed papers are the foundation of all transactions and governance in banks and government agencies, to mention a few examples. Verifying signatures on these documents is an important aspect of the back-office operations. Manual signature verification is not only time intensive, but also prone to mistakes and possible fraud. These flaws may be removed using AI-based automated signature analysis, resulting in considerable efficiency benefits. Based on the Siamese Neural Network, this research proposes an offline solution for signature identification and verification. To construct a feature vector, a collection of global features is utilised.

In this paper, a method is presented based on the cyclic spectral features and the neural network classifiers for performing automatic modulation recognition. The process of the automatic modulation recognition using the method is divided into three basic steps: cyclic spectrum analysis, feature extraction and neural network classifier. Because a new method called as maximum likelihood filter method is used to estimate cyclic spectral density of communication signal, better estimate performance can be acquired in the scene of short length of data. Probability neural network algorithm improves the performance of classifier. Some computer simulation results showing the performance of the method in this paper are improved. The method can efficiently recognize almost all currently used modulation types and the recognition accuracy rate is over 95% at the SNRs of 10 dB.

Signature is an essential biometric modality used in daily life to authenticate one person on his claim. Due to the widespread usage of stamps in office work, banks, forensic documents, etc., it captures the great interest of the researchers for the past many years. With the advancement in the technology and internet, automatic signature verification considered with renewed interest by the researchers. In this paper, we present the state-of-the-art signature verification mechanism and a novel method to perform signature verification in the RGB images. We train our model using advanced deep learning technique (Deep Neural network (DNN)) to differentiate between genuine and forged classes of signatures. The proposed algorithm evaluated on 4NSigComp2010 and 4NSigComp2012 datasets with the same experimental setup. The results showed that the proposed algorithm outperforms the systems presented in the competitions with an EER of 12 and 11.75, respectively.

This work explores human intervention to improve Automatic Signature Verification (ASV). Significant efforts have been made in order to improve the performance of ASV algorithms over the last decades. This work analyzes how human actions can be used to complement automatic systems. Which actions to take and to what extent those actions can help state‐of‐the‐art ASV systems is the final aim of this research line. The analysis at classification level comprises experiments with responses from 500 people based on crowdsourcing signature authentication tasks. The results allow to establish a human baseline performance and comparison with automatic systems. Intervention at feature extraction level is evaluated using a self‐developed tool for the manual annotation of signature attributes inspired in Forensic Document Experts analysis. We analyze the performance of attribute‐based human signature authentication and its complementarity with automatic systems. The experiments are carried out over a public database including the two most popular signature authentication scenarios based on both online (dynamic time sequences including position and pressure) and offline (static images) information. The results demonstrate the potential of human interventions at feature extraction level (by manually annotating signature attributes) and encourage to further research in its capabilities to improve the performance of ASV.

Online and Offline Signature Verification: A Combined Approach

The signature verification is one of the major and general purposes frequently used approach for person’s verification among all the other existing and known biometric-based verification methods. This brought the attention to the development of an automatic signature verification system. In this paper, an off-line signature verification and recognition system based on tree and grid by adopting a feature extraction novel approach such as pixels in tree, eccentricity, and center was used. The problem of using a trained dataset in order to perform the verification was overcome by using only one genuine and test signature at the run time. Decision of the result which was based on authenticity and governed by the maximum correct feature favor, acceptance, and rejection is based on its majority. The usefulness of the proposed approach was acknowledged by the use of experimental results.

Biometric authentication techniques such as lips, face, and eyes are more reliable and efficient than conventional authentication techniques such as password authentication, token, cards, personal identification number, etc. In this research paper, the emphasis has been laid on the speaker identification based on lip features. In this study, we have presented a detailed comparative analysis for speaker identification by using lip features, Principal Component Analysis (PCA), and neural network classifiers. PCA has been used for feature extraction from the six geometric lip features which are height of the outer corners of the mouth, width of the outer corners of the mouth, height of the inner corners of the mouth, width of the inner corners of the mouth, height of the upper lip, and height of the lower lip. These features are then used for training of the network by using different neural network classifiers such as Back Propagation (BP), Radial Basis Function (RBF) and Learning Vector Quantization (LVQ). These approaches are incorporated on "TULIPS1 database, (Movellan, 1995)" which is a small audiovisual database of 12 subjects saying the first 4 digits in English. After the detailed analysis and evaluation a maximum of 91.07% accuracy in speaker recognition is obtained using PCA and RBF. Speaker identification has a wide range of applications such as Audio Processing, Medical data, Finance, Array processing, etc.

Modulation recognition of non-cooperation underwater acoustic communication signals using principal component analysis

With the rapid advancement of capture devices like tablet or smart phone, there is a huge potential for online signature applications that are expected to occupy a large field of researches in forthcoming years. Online handwritten signature encounters difficulty in the verification process because an individual rarely produce exactly the same signature whenever he signs. This difference in the produced signature is referred to as intra-user variability. Verification difficulty occurs especially in the case where the feature extraction and classification algorithms are designed to classify a stable length vector of input features. In this paper, we introduce an efficient algorithm for online signature length normalization by using Up-Sampling and Down-Sampling techniques. Furthermore, online signature verification system is also proposed by using both Principal Component Analysis (PCA) for feature extraction and Artificial Neural Network (ANN) for classification. The SIGMA database, which has more than 6,000 genuine and 2,000 forged signature samples taken from 200 individuals, is used to evaluate the effectiveness of the proposed technique. Based on the tests performed, the proposed technique managed to achieve False Accept Rate (FAR) of 5.5% and False Reject Rate (FRR) of 8.75%.

In many medical applications, feature selection is obvious; but in medical domains, selecting features and creating a feature vector may require more effort. The wavelet transform (WT) technique is used to identify the characteristic points of an electrocardiogram (ECG) signal with fairly good accuracy, even in the presence of severe high-frequency and low-frequency noise. Principal component analysis (PCA) is a suitable technique for ECG data analysis, feature extraction, and image processing — an important technique that is not based upon a probability model. The aim of the paper is to derive better diagnostic parameters for reducing the size of ECG data while preserving morphology, which can be done by PCA. In this analysis, PCA is used for decorrelation of ECG signals, noise, and artifacts from various raw ECG data sets. The aim of this paper is twofold: first, to describe an elegant algorithm that uses WT alone to identify the characteristic points of an ECG signal; and second, to use a composite WT-based PCA method for redundant data reduction and better feature extraction. PCA scatter plots can be observed as a good basis for feature selection to account for cardiac abnormalities. The study is analyzed with higher-order statistics, in contrast to the conventional methods that use only geometric characteristics of feature waves and lower-order statistics. A new algorithm — viz. PCA variance estimator — is developed for this analysis, and the results are also obtained for different combinations of leads to find correlations for feature classification and useful diagnostic information. PCA scatter plots of various chest and augmented ECG leads are obtained to examine the varying orientations of the ECG data in different quadrants, indicating the cardiac events and abnormalities. The efficacy of the PCA algorithm is tested on different leads of 12-channel ECG data; file no. 01 of the Common Standards for Electrocardiography (CSE) database is used for this study. Better feature extraction is obtained for some specific combinations of leads, and significant improvement in signal quality is achieved by identifying the noise and artifact components. The quadrant analysis discussed in this paper highlights the filtering requirements for further ECG processing after performing PCA, as a primary step for decorrelation and dimensionality reduction. The values of the parameters obtained from the results of PCA are also compared with those of wavelet methods.

An enhanced methodof automatic signature verification isdescribed based onmeasurements derived fromtwo accelerometers andthewriting pressure. Modifications totherefer- encegeneration procedure toaccommodate inconsistencies during enrollment andasupervised adaptation procedure arealso given. These procedures areusedinthedesign andimplementation ofa large-scale field testinvolving 248subjects oversixmonths. The central test results wereaTypeIerror rateof1.7 percent andaType IIrate of0.4percent fordeliberate forgers. Theacceptance ratefor random forgery was0.02 percent. I.INTRODUCTION A technique forautomatic signature verification based on accelerometry hasbeendescribed elsewhere (1)-(3). Thisreport gives improvements tothat method, foremost anadditional meas- urement derived fromthewriting pressure, butalso amoreelab- oratedecision strategy including adaptation and the implementation andresults ofa large field testoftheentire system.