Multi-feature extraction and selection in writer-independent off-line signature verification

Rivard, Dominique; Granger, Éric; Sabourin, Robert

doi:10.1007/s10032-011-0180-6

Cited by 76 publications

(83 citation statements)

References 28 publications

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“…For instance, compared to state-of-the-art WI-SV systems (system 3), where both the FV and SV systems rely on a single prototype for authentication, the proposed FV bio-cryptosystem has shown similar accuracy, while employing only 20 features instead of the 555 features present in the SV system [29]. Thus, applying our proposed GLDM learning approach maintained the performance, while decreasing the representation complexity by about 96% (from 555 to only 20 features).…”

Section: Results Of the Gldm-based Fv Systemmentioning

confidence: 99%

“…In this paper, the distance metric defined by Equation (4) is optimized based on a mixture of Feature-Distance (FD) space [28,29] and dissimilarity matrix analysis. Figure 2 illustrates the different distance metric computational spaces.…”

Section: Overviewmentioning

confidence: 99%

“…A set of 30 grid scales is used for each feature type, producing 60 different single scale feature representations. These representations are then fused to produce a FR of huge dimensionality, M = 30,201 [29].…”

Section: Feature Extractionmentioning

confidence: 99%

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Learning Global-Local Distance Metrics for Signature-Based Biometric Cryptosystems

2017

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Biometric traits, such as fingerprints, faces and signatures have been employed in bio-cryptosystems to secure cryptographic keys within digital security schemes. Reliable implementations of these systems employ error correction codes formulated as simple distance thresholds, although they may not effectively model the complex variability of behavioral biometrics like signatures. In this paper, a Global-Local Distance Metric (GLDM) framework is proposed to learn cost-effective distance metrics, which reduce within-class variability and augment between-class variability, so that simple error correction thresholds of bio-cryptosystems provide high classification accuracy. First, a large number of samples from a development dataset are used to train a global distance metric that differentiates within-class from between-class samples of the population. Then, once user-specific samples are available for enrollment, the global metric is tuned to a local user-specific one. Proof-of-concept experiments on two reference offline signature databases confirm the viability of the proposed approach. Distance metrics are produced based on concise signature representations consisting of about 20 features and a single prototype. A signature-based bio-cryptosystem is designed using the produced metrics and has shown average classification error rates of about 7% and 17% for the PUCPR and the GPDS-300 databases, respectively. This level of performance is comparable to that obtained with complex state-of-the-art classifiers.

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Section: Results Of the Gldm-based Fv Systemmentioning

confidence: 99%

Section: Overviewmentioning

confidence: 99%

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Learning Global-Local Distance Metrics for Signature-Based Biometric Cryptosystems

2017

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“…The feature dissimilarity thresholds are modeled in this space. This method is originally introduced by Rivard et al, to develop writer-independent offline signature verification systems [7]. Recently, we adapted these systems for specific users by tuning their universal representations for specific users [8].…”

Section: Application To the Offline Signature Biometricsmentioning

confidence: 99%

A Dissimilarity-Based Approach for Biometric Fuzzy Vaults–Application to Handwritten Signature Images

Eskander

Sabourin

Granger

2013

New Trends in Image Analysis and Processing – ICIAP 2013

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Abstract. Bio-Cryptographic systems enforce authenticity of cryptographic applications like data encryption and digital signatures. Instead of simple user passwords, biometrics, such as, fingerprint and handwritten signatures, are employed to access the cryptographic secret keys. The Fuzzy Vault scheme (FV) is massively employed to produce biocryptogra-phic systems, as it absorbs variability in biometric signals. However, the FV design problem is not well formulated in the literature, and different approaches are applied for the different biometric traits. In this paper, a generic FV design approach, that could be applied to different biometrics, is introduced. The FV decoding functionality is formulated as a simple classifier that operates in a dissimilarity representation space. A boosting feature selection (BFS) method is employed for optimizing this classifier. Application of the proposed approach to offline signature biometrics confirms its viability. Experimental results on the Brazilian signature database (that includes various forgeries) have shown FV recognition accuracy of 90% and system entropy of about 69-bits.

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“…Recent work on the area explore a variety of different feature descriptors: Extended Shadow Code (ESC) and Directional-Probabilistic Density Function (DPDF) [4], [5]; Local Binary Patterns (LBP), Gray-Level Co-occurrence Matrix (GLCM) and Histogram of Oriented Gradients (HOG) [6], [7]; Curvelet transform [8], among others. Instead of relying on hand-engineered feature extractors, we investigate feature learning algorithms applied to this task.…”

Section: Introductionmentioning

confidence: 99%

Analyzing features learned for Offline Signature Verification using Deep CNNs

Hafemann

Sabourin

Oliveira

2016

2016 23rd International Conference on Pattern Recognition (ICPR)

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Abstract-Research on Offline Handwritten Signature Verification explored a large variety of handcrafted feature extractors, ranging from graphology, texture descriptors to interest points. In spite of advancements in the last decades, performance of such systems is still far from optimal when we test the systems against skilled forgeries -signature forgeries that target a particular individual. In previous research, we proposed a formulation of the problem to learn features from data (signature images) in a Writer-Independent format, using Deep Convolutional Neural Networks (CNNs), seeking to improve performance on the task. In this research, we push further the performance of such method, exploring a range of architectures, and obtaining a large improvement in state-of-the-art performance on the GPDS dataset, the largest publicly available dataset on the task. In the GPDS-160 dataset, we obtained an Equal Error Rate of 2.74%, compared to 6.97% in the best result published in literature (that used a combination of multiple classifiers). We also present a visual analysis of the feature space learned by the model, and an analysis of the errors made by the classifier. Our analysis shows that the model is very effective in separating signatures that have a different global appearance, while being particularly vulnerable to forgeries that very closely resemble genuine signatures, even if their line quality is bad, which is the case of slowly-traced forgeries.

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Multi-feature extraction and selection in writer-independent off-line signature verification

Cited by 76 publications

References 28 publications

Learning Global-Local Distance Metrics for Signature-Based Biometric Cryptosystems

Learning Global-Local Distance Metrics for Signature-Based Biometric Cryptosystems

A Dissimilarity-Based Approach for Biometric Fuzzy Vaults–Application to Handwritten Signature Images

Analyzing features learned for Offline Signature Verification using Deep CNNs

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