Forged Signature Distinction Using Convolutional Neural Network for Feature Extraction

Nam, Seung-Soo; Park, Hosung; Seo, Changho; Choi, Daeseon

doi:10.3390/app8020153

Cited by 19 publications

(10 citation statements)

References 21 publications

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“…Neural networks present powerful modeling capabilities and are widely used in many applications. In this section, authors introduce the basic multilayer perceptron (MLP) [25] and the convolutional neural network (CNN) [26][27][28][29][30][31] architecture. The WindNet algorithm proposed in this paper is also described in this chapter.…”

Section: The Proposed Cnn Modelmentioning

confidence: 99%

A Short-Term Wind Speed Forecasting Model by Using Artificial Neural Networks with Stochastic Optimization for Renewable Energy Systems

Huang

Kuo

2018

Energies

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To efficiently manage unstable wind power generation, precise short-term wind speed forecasting is critical. To overcome the challenges in wind speed forecasting, this paper proposes a new convolutional neural network algorithm for short-term forecasting. In this paper, the forecasting performance of the proposed algorithm was compared to that of four other artificial intelligence algorithms commonly used in wind speed forecasting. Numerical testing results based on data from a designated wind site in Taiwan were used to demonstrate the efficiency of above-mentioned proposed learning method. Mean absolute error (MAE) and root-mean-square error (RMSE) were adopted as accuracy evaluation indexes in this paper. Experimental results indicate that the MAE and RMSE values of the proposed algorithm are 0.800227 and 0.999978, respectively, demonstrating very high forecasting accuracy.

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Section: The Proposed Cnn Modelmentioning

confidence: 99%

A Short-Term Wind Speed Forecasting Model by Using Artificial Neural Networks with Stochastic Optimization for Renewable Energy Systems

Huang

Kuo

2018

Energies

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“…Nam et al [39] used a private database that contains real finger-drawn signatures of only 20 persons, collected on a Samsung Galaxy S3. The authors proposed convolutional neural networks (CNN) for feature extraction, trained with genuine and forged signatures.…”

Section: Related Workmentioning

confidence: 99%

“…A smartphone is usually handheld, while a tablet may be placed on the desktop or sustained by the left arm if the writer is right-handed. The consequence is that verification performance is strongly degraded in mobile conditions [2,15,16,[26][27][28][29][30][31][32][33][34][35][36][37][38][39].In the present paper, we study the online signature biometrics in the framework of uncontrolled mobile conditions. The challenging question then is how to improve verification performance in uncontrolled mobile conditions?…”

mentioning

confidence: 99%

Enhancing Security on Touch-Screen Sensors with Augmented Handwritten Signatures

Abazid

Houmani

Garcia-Salicetti

2020

Sensors

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We aim at enhancing personal identity security on mobile touch-screen sensors by augmenting handwritten signatures with specific additional information at the enrollment phase. Our former works on several available and private data sets acquired on different sensors demonstrated that there are different categories of signatures that emerge automatically with clustering techniques, based on an entropy-based data quality measure. The behavior of such categories is totally different when confronted to automatic verification systems in terms of vulnerability to attacks. In this paper, we propose a novel and original strategy to reinforce identity security by enhancing signature resistance to attacks, assessed per signature category, both in terms of data quality and verification performance. This strategy operates upstream from the verification system, at the sensor level, by enriching the information content of signatures with personal handwritten inputs of different types. We study this strategy on different signature types of 74 users, acquired in uncontrolled mobile conditions on a largely deployed mobile touch-screen sensor. Our analysis per writer category revealed that adding alphanumeric (date) and handwriting (place) information to the usual signature is the most powerful augmented signature type in terms of verification performance. The relative improvement for all user categories is of at least 93% compared to the usual signature.Sensors 2020, 20, 933 2 of 21 sensor technology, interoperability, setting several new challenging issues that impact verification performance [2,17].Usually, for improving verification performance, different strategies were exploited in the literature: (i) acquiring signatures in controlled conditions [1-16]; (ii) using a high quality sensor (such as a Wacom tablet) with high temporal and spatial resolution, and able to capture other time functions than pen coordinates, as pen pressure and pen inclination angles [18]; (iii) selecting reference signatures in order to control intra-personal variability [19][20][21]; (iv) extracting several features for signature description (as pressure, speed, and acceleration, etc.) [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16] or by means of a deep neural network [2,[22][23][24][25].However, some of these strategies are no longer possible in the mobile scenario: as pointed out by [17], the sensors are not of the same quality, in terms of temporal resolution in particular, acquisition conditions are highly variable, and some sensors are limited to the capture of only pen coordinates. In the so-called "cloud scenario" [17], users acquire their signatures as they want, standing, sitting or moving, handling the device on the hand at different angles or orientations, or placing it on any support. A smartphone is usually handheld, while a tablet may be placed on the desktop or sustained by the left arm if the writer is right-handed. The consequence is that verification performance is strongly degraded in mobile conditions [2,15,16,[26][27][2...

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“…(a) (b) Although the MLP is very good in modelling and pattern recognition, the convolutional neural network (CNN) [17][18][19][20][21][22] which uses the concept of weight sharing provides better accuracy in highly non-linear problems such as energy load forecasting. The one-dimensional convolution and pooling layer are presented in Figure 2b.…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

An Electricity Price Forecasting Model by Hybrid Structured Deep Neural Networks

2018

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Electricity price is a key influencer in the electricity market. Electricity market trades by each participant are based on electricity price. The electricity price adjusted with the change in supply and demand relationship can reflect the real value of electricity in the transaction process. However, for the power generating party, bidding strategy determines the level of profit, and the accurate prediction of electricity price could make it possible to determine a more accurate bidding price. This cannot only reduce transaction risk, but also seize opportunities in the electricity market. In order to effectively estimate electricity price, this paper proposes an electricity price forecasting system based on the combination of 2 deep neural networks, the Convolutional Neural Network (CNN) and the Long Short Term Memory (LSTM). In order to compare the overall performance of each algorithm, the Mean Absolute Error (MAE) and Root-Mean-Square error (RMSE) evaluating measures were applied in the experiments of this paper. Experiment results show that compared with other traditional machine learning methods, the prediction performance of the estimating model proposed in this paper is proven to be the best. By combining the CNN and LSTM models, the feasibility and practicality of electricity price prediction is also confirmed in this paper.

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Forged Signature Distinction Using Convolutional Neural Network for Feature Extraction

Cited by 19 publications

References 21 publications

A Short-Term Wind Speed Forecasting Model by Using Artificial Neural Networks with Stochastic Optimization for Renewable Energy Systems

A Short-Term Wind Speed Forecasting Model by Using Artificial Neural Networks with Stochastic Optimization for Renewable Energy Systems

Enhancing Security on Touch-Screen Sensors with Augmented Handwritten Signatures

An Electricity Price Forecasting Model by Hybrid Structured Deep Neural Networks

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