Offline Signature Verification using Feature Point Extraction

Gunjal, S.N.; Jalindar, Dange Bapusaheb; Brahmane, Abhay

doi:10.5120/ijca2016909852

Cited by 10 publications

(8 citation statements)

References 9 publications

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“…First, the comparison process for the three algorithms was done on a single image. After that, the number of images increased each time at a rate of (1,4,8,10) images. Table 1 shows the results of comparison process for the three feature detection algorithms depending on the accuracy and run time for each algorithm.…”

Section: Resultsmentioning

confidence: 99%

“…Figure 1 illustrates example of different patterns of signatures for the same individual. The second phase in signature recognition systems is features extraction phase, which include the process of determine and detecting a set of features in the signature image, such as: number of pixel, width, corner and length where this phase consider an important because it's the base of the process of compare biometric samples and verify individuals [4]. Moreover, the signature is a behavioral characteristic of individuals used in the field of biometrics systems in order to verify the identity of individuals and with the increasing use of biometric features in the field of security, the signature appears as a biometric feature that provides a secure means of delegating individuals and ensuring their identity in legal documents.…”

Section: Introductionmentioning

confidence: 99%

“…The feature extraction phase aims at identifying a group of original image features in order to verify purposes through compare them with user sample features. There are two types of features: first type is function features including speed, compression and position such features used in verification systems of the signature online while the second type is the parameter features which is divided into two types, local parameters and global parameters [4]. The feature extraction phase is relying on determine image features with great accuracy through minimize the dimensions of the original image then extract a group of hidden traits in the image, in order to facilitate the process of differentiation between original and fake signatures.…”

Section: Introductionmentioning

confidence: 99%

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Detection and Feature Extraction for Images Signatures

Mohamad¹,

Alsuhimat²,

Mohamed³

et al. 2018

IJET

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The signing process is one of the most important processes used by organizations to ensure the confidentiality of information and to protect it against any unauthorized penetration or access to such information. As organizations and individuals enter the digital world, there is an urgent need for a digital system capable of distinguishing between the original and fraud signature, in order to ensure individuals authorization and determine the powers allowed to them. In this paper, three widely used feature detection algorithms, HARRIS, BRISK (Binary Robust Invariant Scalable Keypoints) and FAST (Features from Accelerated Segment), these algorithms are compared to calculate the run time and accuracy for set of signature images. Three techniques have been applied using (UTSig) dataset; the experiment consisted of four phases: first, applying the techniques on one image, then on four images, then on eight images, finally applying the techniques on ten images where time and accuracy were calculated for each algorithm in the all phases. The results showed that the BRISK algorithm got the best result among the feature detection algorithm in terms of accuracy and the FAST algori thm got the best result among the feature detection algorithm in terms of run time.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Detection and Feature Extraction for Images Signatures

Mohamad¹,

Alsuhimat²,

Mohamed³

et al. 2018

IJET

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show abstract

“…In the authentication process, the authenticated signature is evaluated by compare its features against reference signature. It is used to classify the testing signature into genuine or forgery [27]. Before the image could be authenticate, the differences between reference image's HOG feature value and tested image's HOG value is calculated.…”

Section: Figure 2:feature Extraction Using Hog Techniquementioning

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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“…Firstly, there exists an outstanding variation even between two signatures of the identical person. They never begin from the same function, and neither do they terminate at the equal function (Gunjal, Dange, & Brahmane, 2016).…”

Section: Introductionmentioning

confidence: 99%

Specimen Authentication System Using Pixel-Based Algorithm

Gupit¹,

ABREGANA²,

Catuburan³

et al. 2020

SHERJ

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Specimen Authentication is the process used to recognize an individual's handwritten signature. The pre-process of verifying a signature is Pixel-based. There is a need for an automatic verification system for a signature since the signature has been a means of a person's identification for ages. Offline systems work on the scanned image of a signature. The main objective of the study was to design and develop a Specimen Authentication System to examine signatures. In this study, the researchers verify the off-line signatures by taking a boundary of the entire signature and make the pixel comparison. Signature is acquired using a scanner. The detection process is done after the data acquisition and pre-processing. Pre-processing includes noise removal, grey-scale, manipulation, edge detection. Experimental results show that 50% of the accurate matching with the existing one. There is an important distinction between simple signature Comparisons and dynamic signature verification. Both can be computerized, but a Simple comparison only takes into account what the signature looks like. Dynamic signature verification takes into account how the signature was made. With dynamic signature verification, it is not the shape or looks of the meaningful signature; it is the change in speed, pressure, and timing that occur during the act of signing. Consistency created by natural motion and practice over time creates a recognizable pattern that makes the hand-written signature a natural for biometric identification.

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Offline Signature Verification using Feature Point Extraction

Cited by 10 publications

References 9 publications

Detection and Feature Extraction for Images Signatures

Detection and Feature Extraction for Images Signatures

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

Specimen Authentication System Using Pixel-Based Algorithm

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