Signature recognition

About: Signature recognition is a research topic. Over the lifetime, 2138 publications have been published within this topic receiving 37605 citations.

An off-line signature verification system

Abstract: Signatures continue to be an important biometric because it remains widely used as a means of personal verification and therefore an automatic verification system is needed. In this paper we present an off-line signature verification and recognition system based on a combination of features extracted such as global features, mask features and grid features. The system is trained using a database of signatures. For each person, a centroid feature vector is obtained from a set of his/her genuine samples using the features that were extracted. The centroid signature is then used as a template which is used to verify a claimed signature. To obtain a satisfactory measure of similarity between our template signature and the claimed signature, we use the Euclidean distance in the feature space. The results were very promising and a success rate of 84.1% was achieved using a localized threshold.

Face Recognition System using Artificial Neural Networks Approach

Abstract: Advances in face recognition have come from considering various aspects of this specialized perception problem. Earlier methods treated face recognition as a standard pattern recognition problem; later methods focused more on the representation aspect, after realizing its uniqueness using domain knowledge; more recent methods have been concerned with both representation and recognition, so a robust system with good generalization capability can be built by adopting state-of-the-art techniques from learning, computer vision, and pattern recognition. A face recognition system based on recent method which concerned with both representation and recognition using artificial neural networks is presented. This paper initially provides the overview of the proposed face recognition system, and explains the methodology used. It then evaluates the performance of the system by applying two (2) photometric normalization techniques: histogram equalization and homomorphic filtering, and comparing with euclidean distance, and normalized correlation classifiers. The system produces promising results for face verification and face recognition

Online Context Recognition in Multisensor Systems using Dynamic Time Warping

Abstract: In this paper we present our system for online context recognition of multimodal sequences acquired from multiple sensors The system uses Dynamic Time Warping (DTW) to recognize multimodal sequences of different lengths, embedded in continuous data streams We evaluate the performance of our system on two real world datasets: 1) accelerometer data acquired from performing two hand gestures and 2) NOKIA's benchmark dataset for context recognition The results from both datasets demonstrate that the system can perform online context recognition efficiently and achieve high recognition accuracy

Investigating fusion approaches in multi-biometric cancellable recognition

Abstract: Highlights? We analyzed fusion approaches for cancellable multi-biometric data, with ensembles. ? We adapted three transformation functions (FTs) to be used with voice and iris data. ? We find that individual FTs decrease the accuracy of the voice and iris dataset. ? The combination of transformation functions increased the accuracy of the ensembles. ? The statistical analysis proved the good results reached by combining all three FTs. Cancellable biometrics has recently been introduced in order to overcome some privacy issues about the management of biometric data, aiming to transform a biometric trait into a new but revocable representation for enrolment and identification (verification). Therefore, a new representation of original biometric data can be generated in case of being compromised. Additionally, the use multi-biometric systems are increasingly being deployed in various biometric-based applications since the limitations imposed by a single biometric model can be overcome by these multi-biometric recognition systems. In this paper, we specifically investigate the performance of different fusion approaches in the context of multi-biometrics cancellable recognition. In this investigation, we adjust the ensemble structure to be used for a biometric system and we use as examples two different biometric modalities (voice and iris data) in a multi-biometrics context, adapting three cancellable transformations for each biometric modality.

An Efficient Signature Verification Method Based on an Interval Symbolic Representation and a Fuzzy Similarity Measure

Abstract: In this paper, an efficient offline signature verification method based on an interval symbolic representation and a fuzzy similarity measure is proposed. In the feature extraction step, a set of local binary pattern-based features is computed from both the signature image and its under-sampled bitmap. Interval-valued symbolic data is then created for each feature in every signature class. As a result, a signature model composed of a set of interval values (corresponding to the number of features) is obtained for each individual’s handwritten signature class. A novel fuzzy similarity measure is further proposed to compute the similarity between a test sample signature and the corresponding interval-valued symbolic model for the verification of the test sample. To evaluate the proposed verification approach, a benchmark offline English signature data set (GPDS-300) and a large data set (BHSig260) composed of Bangla and Hindi offline signatures were used. A comparison of our results with some recent signature verification methods available in the literature was provided in terms of average error rate and we noted that the proposed method always outperforms when the number of training samples is eight or more.