Novel Features for Off-line Signature Verification

Majhi, Babita; Reddy, Y Santhosh; Babu, D Prasanna

doi:10.15837/ijccc.2006.1.2268

Cited by 44 publications

(23 citation statements)

References 8 publications

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“…From the comparison (see Table 14) it is clear with the large database size the proposed system yields lower AER (17.33) and hence the performance of the system is encouraging. In literature, an other model which makes use of centroids as features is reported in [17]. However, it employs directly the Euclidean distance between the centroids of a test signature and that of the stored signature and hence it is not invariant to scaling.…”

Section: Comparison With Other Methodsmentioning

confidence: 99%

Offline Signature Verification: An Approach Based on Score Level Fusion

Prakash¹,

Guru²

2010

IJCA

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In this paper, we propose a new approach for offline signature verification based on score level fusion of distance and orientation features of centroids. The proposed method employs symbolic representation of offline signatures using bi-interval valued feature vector. Distance and orientation features of centroids of offline signatures are used to form bi-interval valued symbolic feature vector for representing signatures. A method of offline signature verification based on the bi-interval valued symbolic representation is presented. Several experiments are conducted on MCYT_ signature database [1] of 2250 signatures to demonstrate the efficacy of the proposed approach based score level fusion for offline signature verification.

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Section: Comparison With Other Methodsmentioning

confidence: 99%

Offline Signature Verification: An Approach Based on Score Level Fusion

Prakash¹,

Guru²

2010

IJCA

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“…DCT is [12] a well-known signal analysis tool used in compression due to its compact representation power. It's known that Karhunen-Loeve transform (KLT) is the optimal transform in terms of information packing, however, its data dependent nature makes it infeasible to implement in some practical tasks.…”

Section: Discrete Cosine Transformmentioning

confidence: 99%

A Fusion Approach for Signature Recognition

Roja¹,

Sawarkar²

2012

IJCEE

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Abstract-A new approach for the personal authentication using signatures is presented. This paper attempts to improve the performance of signature based authentication system by integrating multiple algorithms. The signatures are acquired using digital pen tablet and then features are extracted. These features are then examined for their combined performances. Our experimental results on the image data set from 30 users confirm the advantages of our fusion technique using simple image acquisition.

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“…The forgeries involved in handwritten signatures have been categorized based on their characteristic features [5].…”

Section: 35performance Evaluationmentioning

confidence: 99%

“…When only the casual forgeries are considered, AERs of 2.8% and 3.0% are reported. Majhi, Reddy and Prasanna [5] proposed a morphological parameter for signature recognition, they proposed center of mass of signature segments, and the signature was split again and again at its center of mass to obtain a series of points in horizontal as well as vertical mode. The point sequence is then used as discriminating feature; the thresholds were selected separately for each person.…”

Section: 3off-line Signature Recognitionmentioning

confidence: 99%

Off-Line Signature Recognition Systems

Kekre¹,

Bharadi²

2010

IJCA

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Handwritten signature is one of the most widely used biometric traits for authentication of person as well as document. In this paper we discuss issues regarding off-line signature recognitions. We review existing techniques, their performance and method for feature extraction. We discuss a system designed using cluster based global features which is a multi algorithmic offline signature recognition system.

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Novel Features for Off-line Signature Verification

Cited by 44 publications

References 8 publications

Offline Signature Verification: An Approach Based on Score Level Fusion

Offline Signature Verification: An Approach Based on Score Level Fusion

A Fusion Approach for Signature Recognition

Off-Line Signature Recognition Systems

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