An efficient low cost approach for on-line signature recognition based on length normalization and fractional distances

Vivaracho-Pascual, Carlos; Faundez-Zanuy, Marcos; Pascual, Juan M.

doi:10.1016/j.patcog.2008.07.008

Cited by 68 publications

(40 citation statements)

References 15 publications

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“…Normally, this distance calculation is based on the use of the Euclidean one. When data are high dimensional, however, f ractional p-norms (Minkowski-like norms with an exponent p less than one) are, in general, less sensitive to he concentration phenomenon [6], performing better, as can be seen in our work [15].…”

Section: Introductionmentioning

confidence: 52%

“…In [15] we have applied resampling techniques to normalize the signature size, being possible, then, that the similarity between signatures can be computed as a simple distance measurement between vectors. Normally, this distance calculation is based on the use of the Euclidean one.…”

Section: Introductionmentioning

confidence: 99%

“…In Sec. 2 the main characteristics of the baseline system, as shown in [15], will be described. The feature selection method can be seen in Sec.…”

Section: Introductionmentioning

confidence: 99%

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Feature Selection in a Low Cost Signature Recognition System Based on Normalized Signatures and Fractional Distances

Vivaracho-Pascual

Pascual-Gaspar

Cardeñoso-Payo

2009

Advances in Biometrics

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Abstract. In a previous work a new proposal for an efficient on-line signature recognition system with very low computational load and storage requirements was presented. This proposal is based on the use of size normalized signatures, which allows for similarity estimation, usually based on DTW or HMMs, to be performed by an easy distance calcultaion between vectors, which is computed using fractional distance. Here, a method to select representative features from the normalized signatures is presented. Only the most stable features in the training set are used for distance estimation. This supposes a larger reduction in system requirements, while the system performance is increased. The verification task has been carried out. The results achieved are about 30% and 20% better with skilled and random forgeries, respectively, than those achieved with a DTW-based system, with storage requirements between 15 and 142 times lesser and a processing speed between 274 and 926 times greater. The security of the system is also enhanced as only the representative features need to be stored, it being impossible to recover the original signature from these.

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Section: Introductionmentioning

confidence: 52%

Section: Introductionmentioning

confidence: 99%

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Feature Selection in a Low Cost Signature Recognition System Based on Normalized Signatures and Fractional Distances

Vivaracho-Pascual

Pascual-Gaspar

Cardeñoso-Payo

2009

Advances in Biometrics

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“…A wavelet packet entropy adaptive network-based fuzzy inference system was developed to classify twenty 512 × 512 texture images obtained from the Brodatz image album [41]. Reference [42] proposed a features extraction method for speaker recognition based on a combination of three entropy types (i.e., sure, logarithmic energy, and norm) was proposed. The proposed scheme by Sengur [43] is composed of a wavelet domain feature extractor and an ANFIS classifier, where both entropy and energy features were are used in the wavelet domain.…”

Section: Wavelet Transform Entropy For Feature Extractionmentioning

confidence: 99%

Off-Line Handwritten Signature Recognition by Wavelet Entropy and Neural Network

Daqrouq

Sweidan

Balamesh

et al. 2017

Entropy

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Abstract:Handwritten signatures are widely utilized as a form of personal recognition. However, they have the unfortunate shortcoming of being easily abused by those who would fake the identification or intent of an individual which might be very harmful. Therefore, the need for an automatic signature recognition system is crucial. In this paper, a signature recognition approach based on a probabilistic neural network (PNN) and wavelet transform average framing entropy (AFE) is proposed. The system was tested with a wavelet packet (WP) entropy denoted as a WP entropy neural network system (WPENN) and with a discrete wavelet transform (DWT) entropy denoted as a DWT entropy neural network system (DWENN). Our investigation was conducted over several wavelet families and different entropy types. Identification tasks, as well as verification tasks, were investigated for a comprehensive signature system study. Several other methods used in the literature were considered for comparison. Two databases were used for algorithm testing. The best recognition rate result was achieved by WPENN whereby the threshold entropy reached 92%.

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“…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. There are some co mmonly used verification methods, such as template matching methods , statistical based methods, and structural based methods [8][9][10].…”

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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An efficient low cost approach for on-line signature recognition based on length normalization and fractional distances

Cited by 68 publications

References 15 publications

Feature Selection in a Low Cost Signature Recognition System Based on Normalized Signatures and Fractional Distances

Feature Selection in a Low Cost Signature Recognition System Based on Normalized Signatures and Fractional Distances

Off-Line Handwritten Signature Recognition by Wavelet Entropy and Neural Network

Methods of Alignment for On-Line Signature Verification

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