Online Signature Recognition Using Matlab

Shahane, P.R.; Choukade, A.S.; Diyewar, A.N.

doi:10.17148/ijireeice.2015.3222

Cited by 6 publications

(4 citation statements)

References 6 publications

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“…To extract the boundaries of all objects both the Sobel (Gao et al, 2010; Zhang et al, 2009) and Canny (Canny, 1986; Wall et al, 2008) edge detection filters were separately applied to the images for comparison. The Sobel filter detects strong edges in the horizontal and vertical directions, and all the edges are extracted using a threshold value (Shahane et al, 2015; Sobel, 1990). If high threshold values are applied, signals from very small islands or exposed rocks would also disappear along with the signals from ships.…”

Section: Methodsmentioning

confidence: 99%

An Improved Method of Land Masking for Synthetic Aperture Radar-based Ship Detection

2018

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Land masking of Synthetic Aperture Radar (SAR) images is generally accomplished by applying either archived shoreline databases or image segmentation. However, those methods cannot be solely applied to geographical areas complicated with many small islands and exposed rocks. Therefore, we have proposed a new procedure where Sobel edge extraction is applied to detect the edges of all objects from KOMPSAT-5 X-band SAR images, followed by a merging process with the edges from the land objects based on Electronic Navigational Chart (ENC) coastlines. Using the land mask data, geometrically corrected SAR images were masked before applying a ship detection algorithm. This land masking procedure was applied to several images covering different areas of the Korean Peninsula. The results show that land targets such as newly constructed and natural objects were also masked, and thus did not create false alarms during ship detection. Therefore, this method can be used to assist precise ship detection using SAR images in coastal waters.

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Section: Methodsmentioning

confidence: 99%

An Improved Method of Land Masking for Synthetic Aperture Radar-based Ship Detection

2018

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“…Signature verification analyzes the way a user's signs name. Such systems measure features such as speed, velocity, acceleration and pressure (Shahane et al, 2015).…”

Section: Signature Recognitionmentioning

confidence: 99%

Keystroke Dynamics Authentication in Cloud Computing

Hassan

Fouad

Hassan

2015

International Journal of Enterprise Information Systems

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Cloud computing needs a strong and efficient authentication system because the user will access his rented part through a faraway connection and it will make the authentication sensor device besides the user place for identification and verification so how to know the user who claimed himself to be the legal user. Keystroke identification system as a biometric authentication technique is strongly Candidate for the security issues in cloud computing technology. Keystroke dynamics as a security system did not need extra hardware because the authentication device will be the existing keyboard based on everyone has a unique style for writing. The other biometric methods are addressed with each advantage and disadvantage along with keystroke method. In this paper, all known studies about keystroke technique are explained and compared between them according to the classification technique, number of the participated users and each study results then introduces a survey on software and hardware of other biometric authentication techniques and after the literature review is addressed then keystroke as a biometric authentication system is suggested to access cloud computing environment because it has many advantages to being a part of the known security systems which spread in our world.

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“…According to [6], in the off-line signature authentication technique, images of the signatures written on paper are captured using a scanner or a camera. The off-line authentication only covers the images which are obtained from a scanner or digital camera [7]. An off-line signature authentication uses some mechanism as a classifier and some sample signatures are stored in a database [8].…”

Section: Introductionmentioning

confidence: 99%

“…It is a useful application in bank service and forgery detection as it provides an accurate person identification method based on the signature. Yet, although off-line signature authentication is difficult to design [7], it is still essential to authenticate one's identity as most of the financial transactions are still done on paper.…”

Section: Introductionmentioning

confidence: 99%

Histogram of Oriented Gradient (HOG) for Off-Line Handwritten Signature Authentication

Ibrahim¹

2020

IJETER

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Security is needed as it could secure the routine activities particularly when it comes to document-related works. The signature authentication system is being used widely in this era of technology to authenticate an individual's identity. Yet, several problems such as the increasing number of forge signature still occur, which induced a need for an efficient signature authentication system. The human eyes could not differentiate between the genuine and forge signature, and the traditional method to authenticate a signature in financial institutions mainly is very time-consuming. Thus, an off-line handwritten signature authentication using a technique of Histogram of Oriented Gradient (HOG) is proposed. HOG is a gradient-based feature extraction method. Initially, the offline handwritten signature images will go through the pre-processing and image enhancement processes such as binarization, thinning, noise removal, and resizing. Next, the shape feature is extracted using the HOG which produced the feature vector values. Different images will have different feature vector values. These values are then be used to measure the similarity between the genuine and tested signatures using Euclidean distance. Three threshold values are used for the performance analysis of the HOG signature authentication which are 15, 20, and 30. From the analysis conducted, it is found out that 15 and 20 are the best threshold values for HOG which returned good performance of 87.5% for Overall Accuracy (OA). It is expected that the outcome of this study may assist the user in the off-line handwritten signature authentication in the future.

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Online Signature Recognition Using Matlab

Cited by 6 publications

References 6 publications

An Improved Method of Land Masking for Synthetic Aperture Radar-based Ship Detection

An Improved Method of Land Masking for Synthetic Aperture Radar-based Ship Detection

Keystroke Dynamics Authentication in Cloud Computing

Histogram of Oriented Gradient (HOG) for Off-Line Handwritten Signature Authentication

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