Showing papers on "Signature recognition published in 2015"

Biometric Template Protection: Bridging the performance gap between theory and practice

Abstract: Biometric recognition is an integral component of modern identity management and access control systems. Due to the strong and permanent link between individuals and their biometric traits, exposure of enrolled users? biometric information to adversaries can seriously compromise biometric system security and user privacy. Numerous techniques have been proposed for biometric template protection over the last 20 years. While these techniques are theoretically sound, they seldom guarantee the desired noninvertibility, revocability, and nonlinkability properties without significantly degrading the recognition performance. The objective of this work is to analyze the factors contributing to this performance divide and highlight promising research directions to bridge this gap. The design of invariant biometric representations remains a fundamental problem, despite recent attempts to address this issue through feature adaptation schemes. The difficulty in estimating the statistical distribution of biometric features not only hinders the development of better template protection algorithms but also diminishes the ability to quantify the noninvertibility and nonlinkability of existing algorithms. Finally, achieving nonlinkability without the use of external secrets (e.g., passwords) continues to be a challenging proposition. Further research on the above issues is required to cross the chasm between theory and practice in biometric ?template protection.

Robust Biometric Recognition From Palm Depth Images for Gloved Hands

Abstract: Biometric recognition can be used to improve gesture-based interfaces by automatically identifying operators. Traditional palm biometric recognition techniques depend on palm appearance features, but these features are not available in an operating theater where gloves are worn. We propose a depth-based solution for palm biometric recognition. Based on the depth image, our system automatically segments the user's palm and extracts finger dimensions. The finger dimensions are further scaled according to the sensed depth to obtain the true finger dimensions, which are then used as features to characterize the palm. Finally, a modified $k$ -nearest neighbors algorithm that assigns class labels based on the centroid displacement of each class in the neighboring points is applied to recognize the palm based on the geometric features. An accuracy of 96.24% was achieved for the biometric recognition of 4057 gloved palm samples captured at different angles and depths from 27 users. This accuracy is comparable with those of other state-of-the-art classification algorithms and demonstrates that biometric recognition may be viable for settings with gloved hands such as surgery.

Adversarial Biometric Recognition : A review on biometric system security from the adversarial machine-learning perspective

Abstract: In this article, we review previous work on biometric security under a recent framework proposed in the field of adversarial machine learning. This allows us to highlight novel insights on the security of biometric systems when operating in the presence of intelligent and adaptive attackers that manipulate data to compromise normal system operation. We show how this framework enables the categorization of known and novel vulnerabilities of biometric recognition systems, along with the corresponding attacks, countermeasures, and defense mechanisms. We report two application examples, respectively showing how to fabricate a more effective face spoofing attack, and how to counter an attack that exploits an unknown vulnerability of an adaptive face-recognition system to compromise its face templates.

Preprocessing and Feature Selection for Improved Sensor Interoperability in Online Biometric Signature Verification

Abstract: Due to the technological evolution and the increasing popularity of smartphones, people can access an application using authentication based on biometric approaches from many different devices. Device interoperability is a very challenging problem for biometrics, which needs to be further studied. In this paper, we focus on interoperability device compensation for online signature verification since this biometric trait is gaining a significant interest in banking and commercial sector in the last years. The proposed approach is based on two main stages. The first one is a preprocessing stage where data acquired from different devices are processed in order to normalize the signals in similar ranges. The second one is based on feature selection taking into account the device interoperability case, in order to select to select features which are robust in these conditions. This proposed approach has been successfully applied in a similar way to two common system approaches in online signature verification, i.e., a global features-based system and a time functions-based system. Experiments are carried out using Biosecure DS2 (Wacom device) and DS3 (Personal Digital Assistant mobile device) dynamic signature data sets which take into account multisession and two different scenarios emulating real operation conditions. The performance of the proposed global features-based and time functions-based systems applying the two main stages considered in this paper have provided an average relative improvement of performance of 60.3% and 26.5% Equal Error Rate (EER), respectively, for random forgeries cases, compared with baseline systems. Finally, a fusion of the proposed systems has achieved a further significant improvement for the device interoperability problem, especially for skilled forgeries. In this case, the proposed fusion system has achieved an average relative improvement of 27.7% EER compared with the best performance of time functions-based system. These results prove the robustness of the proposed approach and open the door for future works using devices as smartphones or tablets, commonly used nowadays.

Continuous Action Recognition Based on Sequence Alignment

Abstract: Continuous action recognition is more challenging than isolated recognition because classification and segmentation must be simultaneously carried out. We build on the well known dynamic time warping framework and devise a novel visual alignment technique, namely dynamic frame warping (DFW), which performs isolated recognition based on per-frame representation of videos, and on aligning a test sequence with a model sequence. Moreover, we propose two extensions which enable to perform recognition concomitant with segmentation, namely one-pass DFW and two-pass DFW. These two methods have their roots in the domain of continuous recognition of speech and, to the best of our knowledge, their extension to continuous visual action recognition has been overlooked. We test and illustrate the proposed techniques with a recently released dataset (RAVEL) and with two public-domain datasets widely used in action recognition (Hollywood-1 and Hollywood-2). We also compare the performances of the proposed isolated and continuous recognition algorithms with several recently published methods.

Towards Bloom filter-based indexing of iris biometric data

Abstract: Conventional biometric identification systems require exhaustive 1 ∶ N comparisons in order to identify biometric probes, i.e. comparison time frequently dominates the overall computational workload. Biometric database indexing represents a challenging task since biometric data is fuzzy and does not exhibit any natural sorting order. In this paper we present a preliminary study on the feasibility of applying Bloom filters for the purpose of iris biometric database indexing. It is shown, that by constructing a binary tree data structure of Bloom filters extracted from binary iris biometric templates (iris-codes) the search space can be reduced to O(logN). In experiments, which are carried out on a database of N = 256 classes, biometric performance (accuracy) is maintained for different conventional identification systems. Further, perspectives on how to employ the proposed scheme on large-scale databases are given.

Hill-Climbing Attacks on Multibiometrics Recognition Systems

Abstract: Biometric recognition systems, despite the advantages provided with respect to traditional authentication methods, have some peculiar weaknesses which may allow an attacker being falsely recognized or accessing users’ personal data. Among such vulnerabilities, in this paper, we speculate on the hill-climbing attack, i.e., the possibility for an attacker to exploit the scores produced by the matcher with the goal of generating synthetic biometric data, which could allow a false acceptance. More in detail, we focus on multibiometrics systems and investigate about the robustness of different system architectures, both parallel and serial fusion schemes, against the hill-climbing attack. Nonuniform quantization is also evaluated as a possible countermeasure for limiting the effectiveness of the considered attacks in terms of recognition success rate and average number of required attempts without affecting the recognition performance.

Towards an automatic on-line signature verifier using only one reference per signer

Abstract: What can be done with only one enrolled real hand-written signature in Automatic Signature Verification (ASV)? Using 5 or 10 signatures for training is the most common case to evaluate ASV. In the scarcely addressed case of only one available signature for training, we propose to use modified duplicates. Our novel technique relies on a fully neuromuscular representation of the signatures based on the Kinematic Theory of rapid human movements and its Sigma-Lognormal model. This way, a real on-line signature is converted into the Sigma-Lognormal model domain. The model parameters are then varied to generate new duplicated signatures.

Speech Recognition with Hidden Markov Model: A Review

Abstract: The concept of Recognition one phase of Speech Recognition Process using Hidden Markov Model has been discussed in this paper. Preprocessing, Feature Extraction and Recognition three steps and Hidden Markov Model (used in recognition phase) are used to complete Automatic Speech Recognition System. Today's life human is able to interact with computer hardware and related machines in their own language. Research followers are trying to develop a perfect ASR system because we have all these advancements in ASR and research in digital signal processing but computer machines are unable to match the performance of their human utterances in terms of accuracy of matching and speed of response. In case of speech recognition the research followers are mainly using three different approaches namely Acoustic phonetic approach, Knowledge based approach and Pattern recognition approach. This paper's study is based on pattern recognition approach and the third phase of speech recognition process 'Recognition' and Hidden Markov Model is studied in detail.

Biometric template protection for speaker recognition based on universal background models

Abstract: (Voice-) biometric data is considered as personally identifiable information, that is, the increasing demand on (mobile) speaker recognition systems calls for applications which prevent from privacy threats, such as identity-theft or tracking without consent. Technologies of biometric template protection, in particular biometric cryptosystems, fulfil standardised properties of irreversibility and unlinkability which represent appropriate countermeasures to such vulnerabilities of conventional biometric recognition systems. Thereby, public confidence in and social acceptance of biometric applications is strengthened. In this work the authors propose a binarisation technique, which is used to extract scalable high-entropy binary voice reference data (templates) from speaker models, based on Gaussian mixture models and universal background models. Binary feature vectors are then protected within a template protection scheme in particular, fuzzy commitment scheme, in which error correction list-decoding is employed to overcome high intra-class variance of voice samples. In experiments, which are evaluated out on a text-independent speaker corpus of 339 individuals, it is demonstrated that the fully ISO/IEC IS 24745 compliant system achieves privacy protection at a negligible loss of biometric performance, confirming the soundness of the presented approach.

The k-NN classifier and self-adaptive Hotelling data reduction technique in handwritten signatures recognition

Abstract: The paper proposes a novel signature verification concept. This new approach uses appropriate similarity coefficients to evaluate the associations between the signature features. This association, called the new composed feature, enables the calculation of a new form of similarity between objects. The most important advantage of the proposed solution is case-by-case matching of similarity coefficients to a signature features, which can be utilized to assess whether a given signature is genuine or forged. The procedure, as described, has been repeated for each person presented in a signatures database. In the verification stage, a two-class classifier recognizes genuine and forged signatures. In this paper, a broad range of classifiers are evaluated. These classifiers all operate on features observed and computed during the data preparation stage. The set of signature composed features of a given person can be reduced what decrease verification error. Such a phenomenon does not occur for the raw features. The approach proposed was tested in a practical environment, with handwritten signatures used as the objects to be compared. The high level of signature recognition obtained confirms that the proposed methodology is efficient and that it can be adapted to accommodate as yet unknown features. The approach proposed can be incorporated into biometric systems.

Feature Representation for Online Signature Verification.

Abstract: Biometrics systems have been used in a wide range of applications and have improved people authentication. Signature verification is one of the most common biometric methods with techniques that employ various specifications of a signature. Recently, deep learning has achieved great success in many fields, such as image, sounds and text processing. In this paper, deep learning method has been used for feature extraction and feature selection.

Feature-based dynamic signature verification under forensic scenarios

Abstract: Nowadays forensic document examiners (FDE) have to analyse more and more signatures captured by digital devices While they can still use the static image of the signature, it has been proven that the dynamic information contains very discriminative information This paper is focused on dynamic signature recognition applied to forensic scenarios An automatic featured-based or global recognition system is considered as some of the features extracted by these systems could be used by FDE in their work A system comprised of 117 global features is proposed and evaluated with BioSecure DS2 database A subset of 40 features is selected by SFFS algorithm as the optimal feature vector in the development phase Results of 106% EER are achieved for skilled forgeries which improve previous results using similar approaches In addition, a set of selected features have been analysed statistically for genuine and forged signatures in order to obtain useful information that could be used by forensic experts in their reports

Online Signature Recognition Using Software as a Service (SaaS) Model on Public Cloud

Abstract: The signature recognition systems are widely used and measure of security and authenticity in terms of commercial as well as official transactions. The exciting signature recognition systems need a high configuration machine to perform multiple operations of feature vector extraction, enrollment and verification. These implementations are generally standalone and implemented on a single server based architecture, in this case even a single point of failure may occur. The standalone application are not scalable. With the increasing number users the biometric implementation has to be scalable and capable of handling large datasets for a large population. In this paper, a highly scalable, pluggable and faster cloud based online signature recognition system is proposed, which is capable of operating on enormous amounts of data, which, in turn, induces the need for sufficient storage capacity and significant processing power.

State-of-the-Art in Handwritten Signature Verification System

Abstract: Recently, handwritten signature verification (HSV) has become atremendously active area of research. Considerable results have been achieved in terms of accuracy and computation so far. Generally, biometrics can be divided into two types:Behavioral (signature verification, keystroke dynamics, etc.) and Physiological (iris characteristics, fingerprint, etc.). Signature verification is widely studied and discussed by using two approaches, on-line and offline approaches. Offline systems are more applicable and easy to use in comparison with on-line systems in many parts of the world. However, it is considered more difficult than on-line verification due to the lack of dynamic information. This paper presents State-of-Art about both types of HSV Systems. In this paper, we present recent methods used to capture data as well as different methods and techniques used in pre-processing steps. Additionally, current methods used for features extraction and approaches used for verification in signature systems are presented. Finally, we discuss approaches as well as techniques that have been used. In conclusion, we recommend encouraging ideas to be incorporated in the future.

A New Multimodal Biometric Recognition System Integrating Iris, Face and Voice

Abstract: The recognition accuracy of unimodal biometric systems has to contend with a variety of problems such as background noise, noisy data, non-universality, spoof attacks, intra-class variations, inter-class similarities or distinctiveness, interoperability issues. This paper describes a new multimodal biometric system that integrates multiple traits of an individual for recognition, which is able to alleviate the problems faced by unimodal biometric system while improving recognition performance. We have developed a multimodal biometric system by combining iris, face and voice at match score level using simple sum rule. The match scores are normalized by min-max normalization. The identity established by this system is much more reliable and precise than the individual biometric systems. Experimental evaluations are performed on a public dataset demonstrating the accuracy of the proposed system. The effectiveness of proposed system regarding FAR (False Accept Rate) and GAR (Genuine Accept Rate) is demonstrated with the help of MUBI (Multimodal Biometrics Integration) software.

VSig: Hand-gestured signature recognition and authentication with wearable camera

Abstract: Wearable camera is gaining popularity not only as a recording device for law enforcement and hobbyists, but also as a human-computer interface for the next generation wearable technology. It provides a more convenient and portable platform for gesture input than stationary camera, but poses unique challenges due to user movement and scene variation. In this paper, we describe a robust wearable camera based system called VSig for hand-gestured signature recognition and authentication. The proposed method asks the user to virtually sign within the field of the view of the wearable camera. Fingertip is segmented out and tracked to reconstruct the signature. This is followed by signature matching for authentication with the pre-stored signatures of the individual. A dataset named SIGAIR comprising of hand-gestured signatures from 10 individuals has been created and used for testing. An average accuracy of 97.5% is achieved by the proposed method.

Online handwritten signature recognition by length normalization using up-sampling and down-sampling

Abstract: With the rapid advancement of capture devices like tablet or smart phone, there is a huge potential for online signature applications that are expected to occupy a large field of researches in forthcoming years. Online handwritten signature encounters difficulty in the verification process because an individual rarely produce exactly the same signature whenever he signs. This difference in the produced signature is referred to as intra-user variability. Verification difficulty occurs especially in the case where the feature extraction and classification algorithms are designed to classify a stable length vector of input features. In this paper, we introduce an efficient algorithm for online signature length normalization by using Up-Sampling and Down-Sampling techniques. Furthermore, online signature verification system is also proposed by using both Principal Component Analysis (PCA) for feature extraction and Artificial Neural Network (ANN) for classification. The SIGMA database, which has more than 6,000 genuine and 2,000 forged signature samples taken from 200 individuals, is used to evaluate the effectiveness of the proposed technique. Based on the tests performed, the proposed technique managed to achieve False Accept Rate (FAR) of 5.5% and False Reject Rate (FRR) of 8.75%.

A new system for Chinese sign language recognition

Abstract: In this paper, we propose a new system for isolated sign language recognition (SLR) and continuous SLR. In isolated SLR, Histogram of Oriented Displacement is used to describe the trajectories, and multi-SVM is adopted for classification. In continuous SLR, we propose a Dynamic Programming method with warping templates obtained by Dynamic Time Warping (DTW) algorithm. We evaluate our approach with 450 phrases and 180 sentences recorded by Kinect and compare with classical methods, including Hidden Markov Models and state-of-the-art Conditional Random Fields (CRF), Hidden CRF and Latent Dynamic CRF. The experiments demonstrate the effectiveness of our method.

Biometric signature verification

Abstract: In recent years, biometric signature verification BSV has been considered with renewed interest with increasing need of security and individual verification and authentication whether in banks, offices, institutions or other commercial organisations. Biometric signature verification is a behavioural biometric technique as a signature signifies unique behaviour of an individual. It can upgrade online banking using online digital systems for signing which cannot be altered or manipulated. Digital signature pads use algorithms to record the features of the signature, which is used to authenticate a signer during a transaction. This paper aims to present a comprehensive literature survey of the most recent research papers on biometric signature verification. It highlights the most important methods and addresses variations in the methods and features that are being taken up in the most recent research in this field along with the possible extensions.

Update strategies for HMM-based dynamic signature biometric systems

Abstract: Biometric authentication on devices such as smartphones and tablets has increased significantly in the last years One of the most acceptable and increasing traits is the handwriting signature as it has been used in financial and legal agreements scenarios for over a century Nowadays, it is frequent to sign in banking and commercial areas on digitizing tablets For these reasons, it is necessary to consider a new scenario where the number of training signatures available to generate the user template is variable and besides it has to be taken into account the lap of time between them (inter-session variability) In this work we focus on dynamic signature verification The main goal of this work is to study system configuration update strategies of time functions-based systems such as Hidden Markov Model (HMM) and Gaussian Mixture Models (GMM) Therefore, two different cases have been considered First, the usual case of having an HMM-based system with a fixed configuration (ie Baseline System) Second, an HMM-based and GMM-based systems whose configurations are optimized regarding the number of training signatures available to generate the user template The experimental work has been carried out using an extended version of the Signature Long-Term database taking into account skilled and random or zero-effort forgeries This database is comprised of a total of 6 different sessions distributed in a 15-month time span Analyzing the results, the Proposed Systems achieve an average absolute improvement of 46% in terms of EER(%) for skilled forgeries cases compared to the Baseline System whereas the average absolute improvement for the random forgeries cases is of 27% EER These results show the importance of optimizing the configuration of the systems compared to a fixed configuration system when the number of training signatures available to generate the user template increases

Online Kinect Handwritten Digit Recognition Based on Dynamic Time Warping and Support Vector Machine

Abstract: Handwriting in-space from Kinect depth and color information is a challenging task due to the high variability of signature characteristics for difierent individuals. In this paper, a user-friendly human computer interaction system is proposed and implemented based on Kinect handwriting. The flngertip is flrstly tracked by our detection method in every depth frame to generate 3D trajectory of handwriting, and then normalization and smoothing are performed before feature extraction. On this basis, the time sequence feature of 3D signature can be captured as an online character recognition method, and a joint recognition framework is proposed based on DTW and SVM for input vectors of difierent lengths. The evaluation on a handwriting in-space dataset of digits from 0 to 9 shows that the proposed recognition scheme can ofier a high recognition accuracy and a satisfying robustness to noisy data in digit recognition test even with small training number. Therefore, the method can be successfully applied in many Human Computer Interaction applications in real world.

E-biosign: stylus- and finger-input multi-device database for dynamic signature recognition

Abstract: This paper describes the design, acquisition process and a baseline evaluation of e-BioSign, a new database of dynamic signature and handwriting. e-BioSign is comprised of 5 devices in total, threeWacom devices (DTU-500, DTU-530 and STU 1031) specifically designed to capture dynamic signatures and handwriting, and two Samsung general purpose tablets (Samsung Galaxy Note 10.1 and Samsung ATIV). For these two Samsung tablets data is collected using a pen stylus but also the finger to study the performance of signature verification in a mobile scenario. Data was collected in two sessions for 70 subjects, and includes dynamic information of the signature, the full name and number sequences. For signature and the full name skilled forgeries were also performed. A signature baseline evaluation is carried out for a predefined recognition system based on DTW, achieving a benchmark performance for each of the devices. The use of finger for signing achieves good results for the case of random forgeries (less than 1% EER), but the performance is degraded significantly for the case of skilled forgeries compared to the case using the pen stylus.

Contactless biometric hand geometry recognition using a low-cost 3D camera

Abstract: In the past decade, the interest in using 3D data for biometric person authentication has increased significantly, propelled by the availability of affordable 3D sensors The adoption of 3D features has been especially successful in face recognition applications, leading to several commercial 3D face recognition products In other biometric modalities such as hand recognition, several studies have shown the potential advantage of using 3D geometric information, however, no commercial-grade systems are currently available In this paper, we present a contactless 3D hand recognition system based on the novel Intel RealSense camera, the first mass-produced embeddable 3D sensor The small form factor and low cost make this sensor especially appealing for commercial biometric applications, however, they come at the price of lower resolution compared to more expensive 3D scanners used in previous research We analyze the robustness of several existing 2D and 3D features that can be extracted from the images captured by the RealSense camera and study the use of metric learning for their fusion

Face Recognition based on Deep Neural Network

Abstract: In modern life, we see more techniques of biometric features recognition have been used to our surrounding life, especially the applications in telephones and laptops. These biometric recognition techniques contain face recognition, fingerprint recognition and iris recognition. Our work focuses on the face recognition problem and uses a deep learning method, convolutional neural network, to solve it. And we use the Sobel operator to improve our result accuracy. LFW dataset is used for training and testing which gets a considerable result. And we also test our system on other face dataset, which also has a high accuracy on the recognition.

Deep Learning on a Raspberry Pi for Real Time Face Recognition

Abstract: In this paper we describe a fast and accurate pipeline for real-time face recognition that is based on a convolutional neural network (CNN) and requires only moderate computational resources. After training the CNN on a desktop PC we employed a Raspberry Pi, model B, for the classification procedure. Here, we reached a performance of approximately 2 frames per second and more than 97% recognition accuracy. The proposed approach outperforms all of OpenCV’s algorithms with respect to both accuracy and speed and shows the applicability of recent deep learning techniques to hardware with limited computational performance.

Angular-Similarity-Preserving Binary Signatures for Linear Subspaces

Abstract: We propose a similarity-preserving binary signature method for linear subspaces. In computer vision and pattern recognition, linear subspace is a very important representation for many kinds of data, such as face images, action and gesture videos, and so on. When there is a large amount of subspace data and the ambient dimension is high, the cost of computing the pairwise similarity between the subspaces would be high and it requires a large storage space for storing the subspaces. In this paper, we first define the angular similarity and angular distance between the subspaces. Then, based on this similarity definition, we develop a similarity-preserving binary signature method for linear subspaces, which transforms a linear subspace into a compact binary signature, and the Hamming distance between two signatures provides an unbiased estimate of the angular similarity between the two subspaces. We also provide a lower bound of the signature length sufficient to guarantee uniform distance-preservation between every pair of subspaces in a set. Experiments on face recognition, gesture recognition, and action recognition verify the effectiveness of the proposed method.

Modelling errors in a biometric re-identification system

Abstract: The authors consider the problem of ‘re-identification’ where a biometric system answers the question ‘Has this person been encountered before?’ without actually deducing the person's identity. Such a system is vital in biometric surveillance applications and applicable to biometric de-duplication. In such a system, identifiers are created dynamically as and when the system encounters an input probe. Consequently, multiple probes of the same identity may be mistakenly assigned different identifiers, whereas probes from different identities may be mistakenly assigned the same identifier. In this study, they describe a re-identification system and develop terminology as well as mathematical expressions for prediction of matching errors. Furthermore, they demonstrate that the sequential order in which the probes are encountered by the system has a great impact on its matching performance. Experimental analysis based on unimodal and multimodal faces and fingerprint scores confirms the validity of the designed error prediction model, as well as demonstrates that traditional metrics for biometric recognition fail to accurately characterise the error dynamics of a re-identification system.

A survey on fingerprint biometric recognition system

Abstract: Individual recognition is based on biometric characteristics. A biometric system is an automated method of recognizing an individual. It is an evolving technology which is used in various fields like forensics, secured area and security system. Fingerprint method of identification is the oldest and widely used method of authentication used in biometrics. Fingerprint Recognition system designed uses various techniques in order to reduce the False Acceptance Rate (FAR) and False Rejection Rate (FRR) and to improve the performance of the system. In this paper a brief study on the advancement in the techniques used by various authors in the fingerprint recognition system along with their performance improvement is discussed below. Gaps are identified based on the observations and an optimum approach is proposed.