Rough set approach to online signature identification

Almayyan, Waheeda; Own, Hala S.; Zedan, Hussein

doi:10.1016/j.dsp.2011.01.007

Cited by 28 publications

(14 citation statements)

References 27 publications

(36 reference statements)

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“…In online system, signatures can be captured using a variety of input devices like specially designed pens, hand gloves, special tablets, personal digital assistant (PDA) and tracking-camera [2,28]. The tablet can gather the pen X and Y coordinate sequences, total signing duration, number of pen-ups, strokes count and the pressure value of the pen.…”

Section: Data Acquisition and Preprocessingmentioning

confidence: 99%

“…The tablet can gather the pen X and Y coordinate sequences, total signing duration, number of pen-ups, strokes count and the pressure value of the pen. These features extracted from these signatures can be used as expressing one's handwriting habit and individuality, such as pen pressure, velocity in X and Y direction [2], called dynamic information that can be considered as features for next stage. In the other hand, in an offline system, signatures are optically captured by using a scanner and the completed signatures are available as images [9].…”

Section: Data Acquisition and Preprocessingmentioning

confidence: 99%

“…An online system can record dynamic features of the signature that make it difficult to forge. An online system is appropriate to use in real-time applications, such as financial transactions, document authenticity and office automation [2]. This paper is focusing on offline system only.…”

Section: Introductionmentioning

confidence: 99%

“…Signature Verification System (SVS) is a system that identifies and verifies a handwritten signature whether it is genuine or forgeries. Similar to other human biometrics, the system compares the signature information with all the images stored in the database [2]. Usually, it can be done by comparing one-to-one process that includes data acquisition and preprocessing, feature extraction and verification.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Biometric signature verification system based on freeman chain code and k-nearest neighbor

Azmi

Nasien

Omar

2016

Multimed Tools Appl

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Signature is one of human biometrics that may change due to some factors, for example age, mood and environment, which means two signatures from a person cannot perfectly matching each other. A Signature Verification System (SVS) is a solution for such situation. The system can be decomposed into three stages: data acquisition and preprocessing, feature extraction and verification. This paper presents techniques for SVS that uses Freeman chain code (FCC) as data representation. Before extracting the features, the raw images will undergo preprocessing stage; binarization, noise removal, cropping and thinning. In the first part of feature extraction stage, the FCC was extracted by using boundary-based style on the largest contiguous part of the signature images. The extracted FCC was divided into four, eight or sixteen equal parts. In the second part of feature extraction, six global features were calculated against split image to test the feature efficiency. Finally, verification utilized Euclidean distance to measured and matched in k-Nearest Neighbors. MCYT bimodal database was used in every stage in the system. Based on the experimental results, the lowest error rate for FRR and FAR were 6.67 % and 12.44 % with AER 9.85 % which is better in term of performance compared to other works using that same database.

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Section: Data Acquisition and Preprocessingmentioning

confidence: 99%

Section: Data Acquisition and Preprocessingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Biometric signature verification system based on freeman chain code and k-nearest neighbor

Azmi

Nasien

Omar

2016

Multimed Tools Appl

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“…Signatures would not keeping higher consistent for a long time since external and internal environment exchanging. Thus, it is important to effectively align the test signatures to references, and reduce the influences the inconsistency of the size, location and rotation angle on the performances of online handwritten signature verification [3][4][5][6][7]. During online signature verification process, the authenticity of test signature is evaluated by matching its features against those stored in knowledge base for given individual.…”

Section: Introductionmentioning

confidence: 99%

Methods of Alignment for On-Line Signature Verification

Xia¹,

Luan²,

Wang³

et al. 2015

Proceedings of the International Conference on Logistics, Engineering, Management and Computer Science

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Abstract-S ize, location and rotation angle of on-line handwritten signature will have effects on the performance of verification. Methods of alignment for online signature verification are proposed in this paper, the size, location and rotation angle of test and reference signatures are aligned effectively to reduce the variations of different inputting. A method of S CLC-DTW with signature curves location constraint is proposed also to improved efficiency of similarity calculation. Several experiments are carried out on standard on-line signature dataset DB1, which consists of 5000 signatures from 100 individuals in total. The best result is given by EER=2.99%, which indicates the effectiveness of our proposed methods.

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