Showing papers on "Signature recognition published in 2011"

Feature selection for support vector machine-based face-iris multimodal biometric system

Abstract: Multimodal biometric can overcome the limitation possessed by single biometric trait and give better classification accuracy. This paper proposes face-iris multimodal biometric system based on fusion at matching score level using support vector machine (SVM). The performances of face and iris recognition can be enhanced using a proposed feature selection method to select an optimal subset of features. Besides, a simple computation speed-up method is proposed for SVM. The results show that the proposed feature selection method is able improve the classification accuracy in terms of total error rate. The support vector machine-based fusion method also gave very promising results.

Context-based biometric key generation for Iris

Abstract: In this study, a generic treatment of how to generate biometric keys from binary biometric templates is presented. A context-based analysis of iris biometric feature vectors based on which stable biometric keys are extracted is proposed. Most reliable bits in binary iris codes are detected and utilised to construct keys from fuzzy biometric data. The proposed key-generation scheme is adapted to diverse iris biometric feature extraction algorithms, evaluated on a comprehensive database and compared against existing iris biometric cryptosystems. In addition, the scheme is extended to provide fully revocable biometric keys, long enough to be applied in generic cryptosystems. Experimental results confirm the soundness of the approach.

State-of-the-art in offline signature verification system

Abstract: Biometrics can be classified into two types Behavioral (signature verification, keystroke dynamics, etc.) and Physiological (iris characteristics, fingerprint, etc.). Handwritten signature is one of the first few biometrics used even before computers. Signature verification is widely studied and discussed using two approaches. On-line approach and offline approach. 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 the State-of-the-Art about offline signature verification system; this biometric identification method that had more attraction in recent years because of its necessity for use in daily life routines and when the signature needs to be immediately verified like bank checks. In this paper, we present signature forgery types, features types and recent methods used for features extraction in signature verification systems and approaches used for verification in signature systems. Then we discuss these approaches and for which type of forgeries its suitable. Finally, we suggest new interesting ideas to be incorporated in the future.

A survey of on-line signature verification

Abstract: Signature is a commonly accepted biometric feature for individual identification. On-line signature has begun to prevail in the last decades for it exploits dynamic features which traditional off-line signature fails to preserve. This paper presents a review of researches on on-line signature verification during recent years and lists some of the works that provide promising results.

Deep learning networks for off-line handwritten signature recognition

Abstract: Reliable identification and verification of off-line handwritten signatures from images is a difficult problem with many practical applications. This task is a difficult vision problem within the field of biometrics because a signature may change depending on psychological factors of the individual. Motivated by advances in brain science which describe how objects are represented in the visual cortex, advanced research on deep neural networks has been shown to work reliably on large image data sets. In this paper, we present a deep learning model for off-line handwritten signature recognition which is able to extract high-level representations. We also propose a two-step hybrid model for signature identification and verification improving the misclassification rate in the well-known GPDS database.

Sparsity-motivated automatic target recognition

Abstract: We present an automatic target recognition algorithm using the recently developed theory of sparse representations and compressive sensing. We show how sparsity can be helpful for efficient utilization of data for target recognition. We verify the efficacy of the proposed algorithm in terms of the recognition rate and confusion matrices on the well known Comanche (Boeing–Sikorsky, USA) forward-looking IR data set consisting of ten different military targets at different orientations.

Efficient on-line signature recognition based on multi-section vector quantization

Abstract: This paper proposes a multi-section vector quantization approach for on-line signature recognition. We have used a database of 330 users which includes 25 skilled forgeries performed by 5 different impostors. This database is larger than those typically used in the literature. Nevertheless, we also provide results from the SVC database. Our proposed system obtains similar results as the state-of-the-art online signature recognition algorithm, Dynamic Time Warping, with a reduced computational requirement, around 47 times lower. In addition, our system improves the database storage requirements due to vector compression, and is more privacy-friendly because it is not possible to recover the original signature using the codebooks. Experimental results reveal that our proposed multi-section vector quantization achieves a 98% identification rate, minimum Detection Cost Function value equal to 2.29% for random forgeries and 7.75% for skilled forgeries.

Eigen-signature: A robust and an efficient offline signature verification algorithm

Abstract: An offline signature verification system is proposed in this paper. The proposed model has two stages: preprocessing and eigen-signature construction. In the preprocessing stage, we convert a scanned signature to a shape form and eigen-signature construction is proposed for extracting the feature vector from a shape formed signature. Experiments have been conducted on the newly created Kannada offline signature database to exhibit the performance of the proposed model. A comparative analysis is provided with the recently proposed texture features based offline signature verification system on the publicly available gray signature database to exhibit the performance of the proposed model.

Face recognition using multimodal biometric features

Abstract: This paper presents a new multimodal biometric approach using face and periocular biometric. The available face recognition algorithm performance in presence of multiple variations such as illumination, pose, expression, occlusion and plastic surgery is not satisfactory. Also, periocular biometrics face problems in presence of spectacles, head angle, hair and expression. A method which can extract multiple feature information from a single source and can give a satisfactory performance even with less number of training images is desirable. Thus combining face and periocular data obtained from the same image may increase the performance of the recognition system. A detailed performance analysis of face recognition and periocular biometric using Gabor and LBP features is carried out. This is then compared with the proposed multimodal biometric feature extraction technique. The experimental results obtained using Muct and plastic surgery face database shows that the proposed multimodal biometric performs better than other face recognition and individual biometric methods.

Off-line Signature Identification Using Background and Foreground Information

Abstract: Biometric systems play an important role in the field of information security as they are extremely required for user authentication. Automatic signature recognition and verification is one of the biometric techniques, which is currently receiving renewed interest and is only one of several techniques used to verify the identities of individuals. Signatures provide a secure means for confirmation and authorization in legal documents. So nowadays, signature identification and verification becomes an essential component in automating the rapid processing of documents containing embedded signatures. In this paper, a technique for a bi-script off-line signature identification system is proposed. In the proposed signature identification system, the signatures of English and Bengali (Bangla) are considered for the identification process. Different features such as under sampled bitmaps, modified chain-code direction features and gradient features computed from both background and foreground components are employed for this purpose. Support Vector Machines (SVMs) and Nearest Neighbour (NN) techniques are considered as classifiers for signature identification in the proposed system. A database of 1554 English signatures and 1092 Bengali signatures are used to generate the experimental results. Various results based on different features are calculated and analysed. The highest accuracies of 99.41%, 98.45% and 97.75% are obtained based on the modified chain-code direction, under-sampled bitmaps and gradient features respectively using 1800 (1100 English+700 Bengali) samples for training and 846 (454 English+392 Bengali) samples for testing.

Handwritten signature identification using basic concepts of graph theory

Abstract: Handwritten signature is being used in various applications on daily basis. The problem arises when someone decides to imitate our signature and steal our identity. Therefore, there is a need for adequate protection of signatures and a need for systems that can, with a great degree of certainty, identify who is the signatory. This paper presents previous work in the field of signature and writer identification to show the historical development of the idea and defines a new promising approach in handwritten signature identification based on some basic concepts of graph theory. This principle can be implemented on both on-line handwritten signature recognition systems and off-line handwritten signature recognition systems. Using graph norm for fast classification (filtration of potential users), followed by comparison of each signature graph concepts value against values stored in database, the system reports 94.25% identification accuracy.

Quantitative evaluation of the effects of aging on biometric templates

Abstract: The long-term performance of biometric authentication systems is highly depended on the permanence of biometric features stored in biometric templates. Aging variation causes modifications on biometric features that affect the matching between stored and captured biometric templates causing in that way deterioration in the performance of biometric authentication systems. In this study the authors attempt to quantify the effects of aging for different biometric modalities, so that it is possible to draw conclusions related to the effect of aging on different types of biometric templates. In this context variations between distributions containing biometric features from different age groups are quantified, allowing in that way the definition of age-sensitive and age-invariant biometric features. An important aspect of the proposed approach is the standardised and generic nature of the approach that allows the derivation of comparative results between different modalities and different feature vectors. The work presented in this study provides a valuable tool for selecting, either age-invariant features for use in identity authentication applications, or for selecting age-sensitive features for age-estimation-related applications.

An efficient offline signature identification method based on Fourier Descriptor and chain codes

Abstract: This paper proposes a novel offline signature identification method based on Fourier Descriptor (FD) and Chain Codes features. Signature identification was classified into two different problems: recognition and verification. In recognition process, we used Principle Component Analysis. In verification process, we designed a multilayer feed forward artificial neural network. The main steps of constructing a signature identification system are discussed and experiments on real data sets show that the average error rate can reach 3.8%.

Offline Handwritten Signature Identification and Verification Using Multi-Resolution Gabor Wavelet

Abstract: In this paper, we are proposing a new method for offline (static) handwritten signature identification and verification based on Gabor wavelet transform. The whole idea is offering a simple and robust method for extracting features based on Gabor Wavelet which the dependency of the method to the nationality of signer has been reduced to its minimal. After pre-processing stage, that contains noise reduction and signature image normalisation by size and rotation, a virtual grid is placed on the signature image. Gabor wavelet coefficients with different frequencies and directions are computed on each points of this grid and then fed into a classifier. The shortest weighted distance has been used as the classifier. The weight that is used as the coefficient for computing the shortest distance is based on the distribution of instances in each of signature classes. As it was pointed out earlier, one of the advantages of this system is its capability of signature identification and verification of different nationalities; thus it has been tested on four signature dataset with different nationalities including Iranian, Turkish, South African and Spanish signatures. Experimental results and the comparison of the proposed system with other systems are consistent with desirable outcomes. Despite the use of the simplest method of classification i.e. the nearest neighbour, the proposed algorithm in comparison with other algorithms has very good capabilities. Comparing the results of our system with the accuracy of human's identification and verification, it shows that human identification is more accurate but our proposed system has a lower error rate in verification.

Speech Recognition Using Hidden Markov Model

Abstract: Hidden Markov Models (HMMs) are widely used in pattern recognition applications, most notably speech recognition. Speech samples are recorded using a wave surfer tool. Wave surfer is a simple but powerful interface. The sound can be visualized and analyzed in several ways with the help of this tool. The recorded signal (test data) is compared with the original signal (trained data) using Hidden Markov Model algorithms. This speech recognition is simulated in Matlab.

Learning Mahalanobis distance for DTW based online signature verification

Abstract: Signature, a form of handwritten depiction, has been and is still widely used as a proof of the writer's identity/intent in human society. Online signatures represents the dynamic process of handwriting as a sequence of feature vectors along time. Dynamic time warping (DTW) has been popularly adopted to compare sequence data. A basic problem in using DTW for signature verification is how to estimate the difference between the feature vectors. Most previous researches made use of Euclidean distance (ED) for this problem. However, ED treats each feature equally and cannot take account of the correlations between features. To overcome this problem, this paper proposed Mahalanobis distance (MD) for signature verification. One key question is how to estimate covariance matrix in MD calculation. We formulate this problem in a learning framework and introduce two criterion for estimating the matrix. The first criteria aims at minimizing the signature difference for the same writer, while the second criteria try to maximize the signature difference between different writers while minimize the within-writer signature difference. We carried out experiments on the MCYT biometric database. The experimental results exhibit that the proposed MD based method achieved better results than ED based method.

A Multimodal Biometric Recognition of Touched Fingerprint and Finger-Vein

Abstract: multimodal biometric systems have been widely used to overcome the limitation of unimodal biometric systems and to achieve high recognition accuracy. However, users feel inconvenience because most of the multimodal systems require several steps in order to acquire multimodal biometric data, which also requires the specific behaviors of users. In this research, we propose a new multimodal biometric recognition of touched fingerprint and finger-vein. This paper is novel in the following four ways. First, we can get a fingerprint and a finger-vein image at the same time by the proposed device, which acquires the fingerprint and finger-vein images from the first and second knuckles of finger, respectively. Second, the device's size is so small that we can adopt it on a mobile device, easily. Third, fingerprint recognition is done based on the minutia points of ridge area and finger-vein recognition is performed based on local binary pattern (LBP) with appearance information of finger area. Fourth, based on decision level fusion, we combined two results of fingerprint and finger-vein recognition. Experimental results confirmed the efficiency and usefulness of the proposed method.

UR3D-C: Linear dimensionality reduction for efficient 3D face recognition

Abstract: We present a novel approach for computing a compact and highly discriminant biometric signature for 3D face recognition using linear dimensionality reduction techniques. Initially, a geometry-image representation is used to effectively resample the raw 3D data. Subsequently, a wavelet transform is applied and a biometric signature composed of 7,200 wavelet coefficients is extracted. Finally, we apply a second linear dimensionality reduction step to the wavelet coefficients using Linear Discriminant Analysis and compute a compact biometric signature. Although this biometric signature consists of just 57 coefficients, it is highly discriminant. Our approach, UR3D-C, is experimentally validated using four publicly available databases (FRGC v1, FRGC v2, Bosphorus and BU-3DFE). State-of-the-art performance is reported in all of the above databases.

Retinal recognition: Personal identification using blood vessels

Abstract: Biometrics are used for personal recognition based on some physiologic or behavioral characteristics. In this era, biometric security systems are widely used which mostly include fingerprint recognition, face recognition, iris and speech recognition etc. Retinal recognition based security systems are very rare due to retina acquisition problem but still it provides the most reliable and stable mean of biometric identification. This paper presents a four stage personal identification system using vascular pattern of human retina. In first step, it acquires and preprocesses the colored retinal image. Then blood vessels are enhanced and extracted using 2-D wavelet and adaptive thresholding respectively. In third stage, it performs feature extraction and filtration followed by vascular pattern matching in forth step. The proposed method is tested on three publicly available databases i.e DRIVE, STARE and VARIA. Experimental results show that the proposed method achieved an accuracy of 0.9485 and 0.9761 for vascular pattern extraction and personal recognition respectively.

Offline Signature Recognition & Verification using Neural Network

Abstract: The signature of a person is an important biometric attribute of a human being which can be used to authenticate human identity. However human signatures can be handled as an image and recognized using computer vision and neural network techniques. With modern computers, there is need to develop fast algorithms for signature recognition. There are various approaches to signature recognition with a lot of scope of research. In this paper, off-line signature recognition & verification using neural network is proposed, where the signature is captured and presented to the user in an image format. Signatures are verified based on parameters extracted from the signature using various image processing techniques. The Off-line Signature Recognition and Verification is implemented using C# (C-sharp). This work has been tested and found suitable for its purpose.

Ecg in biometric recognition: time dependency and application challenges

Abstract: As biometric recognition becomes increasingly popular, the fear of circumvention, obfuscation and replay attacks is a rising concern. Traditional biometric modalities such as the face, the fingerprint or the iris are vulnerable to such attacks, which defeats the purpose of biometric recognition, namely to employ physiological characteristics for secure identity recognition. This thesis advocates the use the electrocardiogram (ECG) signal for human identity recognition. The ECG is a vital signal of the human body, and as such, it naturally provides liveness detection, robustness to attacks, universality and permanence. In addition, ECG inherently satisfies uniqueness requirements, because the morphology of the signal is highly dependent on the particular anatomical and geometrical characteristics of the myocardium in the heart. However, the ECG is a continuous signal, and this presents a great challenge to biometric recognition. With this modality, instantaneous variability is expected even within recordings of the same individual due to a variety of factors, including recording noise, or physical and psychological activity. While the noise and heart rate variations due to physical exercise can be addressed with appropriate feature extraction, the effects of emotional activity on the ECG signal are more obscure. This thesis deals with this problem from an affective computing point of view. First, the psychological conditions that affect the ECG and endanger biometric accuracy are identified. Experimental setups that are targeted to provoke active and passive arousal as well as positive and negative valence are presented. The empirical mode decomposition (EMD) is used as the basis for the detection of emotional patterns, after adapting the algorithm to the particular needs of the ECG signal. Instantaneous frequency and oscillation features are used for state classification in various clustering setups. The result of this analysis is the designation of psychological states which affect the ECG signal to an extent that biometric matching may not be feasible. An updating methodology is proposed to address this problem, wherein the signal is monitored for instantaneous changes that require the design of a new template. Furthermore, this thesis presents the enhanced Autocorrelation- Linear Discriminant Analysis (AC/LDA) algorithm for feature extraction, which incorporates a signal quality assessment module based on the periodicity transform. Three deployment scenarios are considered namely a) small-scale recognition systems, b) large-scale recognition systems and c) recognition in distributed systems. The enhanced AC/LDA algorithm is adapted to each setting, and the advantages and disadvantages of each scenario are discussed. Overall, this thesis attempts to provide the necessary algorithmic and practical framework for the real-life deployment of the ECG signal in biometric recognition.

Bioconvolving: Cancelable templates for a multi-biometrics signature recognition system

Abstract: Recently, significant efforts have being dedicated to the design of algorithms and architectures able to protect biometric characteristics, in order to guarantee the necessary security and privacy to their owners. In this paper we discuss a protected on-line signature-based biometric authentication system, where the considered biometrics are secured by means of non-invertible transformations, able to generate templates from which retrieving the original information is computationally as hard as random guessing it. The advantages of using a protection method based on non-invertible transforms are exploited by presenting three different matching strategies in the transformed domain, and by proposing a multi-biometrics approach based on score-level fusion to improve the performances of the considered system. The reported experimental results, evaluated on the public MCYT signature database, show that the achievable recognition rates are only slightly affected by the proposed protection scheme, which is able to provide security and renewability for the considered biometrics.

Evaluation of multiclass support vector machine classifiers using optimum threshold-based pruning technique

Abstract: Support vector machine (SVM) is the state-of-the-art classifier used in real world pattern recognition applications. One of the design objectives of SVM classifiers using non-linear kernels is reducing the number of support vectors without compromising the classification accuracy. To meet this objective, decision-tree approach and pruning techniques are proposed in the literature. In this study, optimum threshold (OT)-based pruning technique is applied to different decision-tree-based SVM classifiers and their performances are compared. In order to assess the performance, SVM-based isolated digit recognition system is implemented. The performances are evaluated by conducting various experiments using speaker-dependent and multispeaker-dependent TI46 database of isolated digits. Based on this study, it is found that the application of OT technique reduces the minimum time required for recognition by a factor of 1.54 and 1.31, respectively, for speaker-dependent and multispeaker-dependent cases. The proposed approach is also applicable for other SVM-based multiclass pattern recognition systems such as target recognition, fingerprint classification, character recognition and face recognition.

Signature Neural Networks: Definition and Application to Multidimensional Sorting Problems

Abstract: In this paper we present a self-organizing neural network paradigm that is able to discriminate information locally using a strategy for information coding and processing inspired in recent findings in living neural systems. The proposed neural network uses: (1) neural signatures to identify each unit in the network; (2) local discrimination of input information during the processing; and (3) a multicoding mechanism for information propagation regarding the who and the what of the information. The local discrimination implies a distinct processing as a function of the neural signature recognition and a local transient memory. In the context of artificial neural networks none of these mechanisms has been analyzed in detail, and our goal is to demonstrate that they can be used to efficiently solve some specific problems. To illustrate the proposed paradigm, we apply it to the problem of multidimensional sorting, which can take advantage of the local information discrimination. In particular, we compare the results of this new approach with traditional methods to solve jigsaw puzzles and we analyze the situations where the new paradigm improves the performance.

Off-line signature verification and recognition: Neural network approach

Abstract: This paper discusses signature verification and recognition using a new approach that depends on a neural network which enables the user to recognize whether a signature is original or a fraud. The user introduces into the computer the scanned images, modifies their quality by image enhancement and noise reduction techniques, to be followed by feature extraction and neural network training, and finally verifies the authenticity of the signature. The paper discusses the different stages of the process including: image pre-processing, feature extraction and pattern recognition through neural networks.

Quality Analysis of Dynamic Signature Based on the Sigma-Lognormal Model

Abstract: An analysis of the quality of on-line handwritten signatures is carried out based on the Sigma-Lognormal model. In the study, two main issues are addressed from a kinematic perspective of humanly-produced movements. On the one hand, what makes some signatures perform better than others in automatic signature verification systems, and on the other hand if that information may be used as a quality measure in order to predict the expected performance of a given sample. Experiments were carried out on the MCYT database and show the high potential of certain kinematic features for signature quality assessment.