Handwritten static signature verification performed using wavelet transforms and neural networks

1998 ◽  
Author(s):  
Daniel K. R. McCormack ◽  
John F. Pedersen
Keyword(s):  
Neural Networks ◽  
Wavelet Transforms ◽  
Signature Verification

fNIR data classification using wavelet transforms and neural networks for attention monitoring

2008 ◽  
Author(s):  
Alireza Akhbardeh ◽  
Meltem Izzetoglu ◽  
Scott Bunce ◽  
Kambiz Pourrezaei ◽  
Banu Onaral
Keyword(s):  
Neural Networks ◽  
Wavelet Transforms ◽  
Data Classification

Neural Networks on Handwritten Signature Verification

2011 ◽  
pp. 1232-1237
Author(s):  
J. Francisco Vargas ◽  
Miguel A. Ferrer
Keyword(s):  
Neural Networks ◽  
Pattern Recognition ◽  
Template Matching ◽  
Personal Identification ◽  
Signature Verification ◽  
Biometric Authentication ◽  
Cognitive Approach ◽  
Fractal Approach ◽  
Pattern Recognition Methods ◽  
Handwritten Signature

Biometric offers potential for automatic personal identification and verification, differently from other means for personal verification; biometric means are not based on the possession of anything (as cards) or the knowledge of some information (as passwords). There is considerable interest in biometric authentication based on automatic signature verification (ASV) systems because ASV has demonstrated to be superior to many other biometric authentication techniques e.g. finger prints or retinal patterns, which are reliable but much more intrusive and expensive. An ASV system is a system capable of efficiently addressing the task of make a decision whether a signature is genuine or forger. Numerous pattern recognition methods have been applied to signature verification. Among the methods that have been proposed for pattern recognition on ASV, two broad categories can be identified: memory-based and parameter-based methods as a neural network. The Major approaches to ASV systems are the template matching approach, spectrum approach, spectrum analysis approach, neural networks approach, cognitive approach and fractal approach. The proposed article reviews ASV techniques corresponding with approaches that have so far been proposed in the literature. An attempt is made to describe important techniques especially those involving ANNs and assess their performance based on published literature. The paper also discusses possible future areas for research using ASV.

scholarly journals 0452 Morlet Wavelet Transforms and Neural Networks in Polysomnography Analysis

SLEEP ◽  
2020 ◽  
Vol 43 (Supplement_1) ◽  
pp. A173-A174
Author(s):  
R Stretch ◽  
M Zeidler
Keyword(s):  
Neural Networks ◽  
Wavelet Transforms ◽  
Sleep Stage ◽  
Scoring Systems ◽  
Time Frequency ◽  
Use Of Time ◽  
Lower Stage ◽  
Data Representations ◽  
Validation Set ◽  
Larger Sample

Abstract Introduction Manually scoring polysomnograms is both time-consuming and labor-intensive. It also increases variability in care. Generating features for use as components within larger models is an important part of building highly accurate auto-scoring systems. In this study, we examined the use of time-frequency data representations in combination with a convolutional neural networks (CNN). Methods We used just six (6) pre-scored polysomnograms from the MrOS dataset in this analysis. Only one electroencephalography (EEG) and one electrooculography (EOG) channel were extracted from each polysomnogram and split into 30 second epochs. Visual representations of each epoch in the time-frequency domain were generated using Morlet wavelets, then divided into training and validation sets in a 4:5 distribution. We then re-trained a ResNet-50 CNN using transfer learning to classify sleep stage based on the time-frequency representations. Results A total of 4971 epochs were generated. Of those, 1242 epochs formed the validation set. Performance was high for identifying Stage W with an accuracy of 94.2% (295/313 epochs). However, performance for other stages was considerably lower. Stage N3 was predicted correctly in 68.0% of cases (138/203 epochs), although in 60/75 cases of misclassification the predicted class was Stage N2. Similarly, Stage N2 was predicted correctly in 62.0% of cases (183/295 epochs), and in 63/112 cases of misclassification the predicted class was Stage N3. Accuracy for Stage REM was 64.9%. Stage N1 prediction was poor (22.0% accuracy), likely due to insufficient representation in the sample (< 10% of epochs). Conclusion This exploratory analysis of the use of time-frequency representations in conjunction with a CNN demonstrates some promise, especially with respect to prediction of Stage W using this technique. Inclusion of additional data channels and larger sample size would likely improve accuracy. Support RS - ASPIRE Fellowship (sponsored by the American Thoracic Association).

scholarly journals Detection and Classification of Power Quality Disturbancewaveform Using MRA Based Modified Wavelet Transfrom and Neural Networks

2010 ◽  
Vol 61 (4) ◽  
pp. 235-240 ◽  
Author(s):  
Perumal Chandrasekar ◽  
Vijayarajan Kamaraj
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Power Quality ◽  
Wavelet Transforms ◽  
Back Propagation ◽  
Voltage Sag ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Detection and Classification of Power Quality Disturbancewaveform Using MRA Based Modified Wavelet Transfrom and Neural Networks In this paper, the modified wavelet based artificial neural network (ANN) is implemented and tested for power signal disturbances. The power signal is decomposed by using modified wavelet transform and the classification is carried by using ANN. Discrete modified wavelet transforms based signal decomposition technique is integrated with the back propagation artificial neural network model is proposed. Varieties of power quality events including voltage sag, swell, momentary interruption, harmonics, transient oscillation and voltage fluctuation are used to test the performance of the proposed approach. The simulation is carried out by using MATLAB software. The simulation results show that the proposed scheme offers superior detection and classification compared to the conventional approaches.

Optical neural networks based on wavelet transforms

2002 ◽  
Author(s):  
Chunling Fan ◽  
Zhihua Jin ◽  
Weifeng Tian
Keyword(s):  
Neural Networks ◽  
Wavelet Transforms ◽  
Optical Neural Networks
Sign in / Sign up

Export Citation Format

Share Document