Showing papers on "Signature recognition published in 2014"

Soft Biometrics and Their Application in Person Recognition at a Distance

Abstract: Soft biometric information extracted from a human body (e.g., height, gender, skin color, hair color, and so on) is ancillary information easily distinguished at a distance but it is not fully distinctive by itself in recognition tasks. However, this soft information can be explicitly fused with biometric recognition systems to improve the overall recognition when confronting high variability conditions. One significant example is visual surveillance, where face images are usually captured in poor quality conditions with high variability and automatic face recognition systems do not work properly. In this scenario, the soft biometric information can provide very valuable information for person recognition. This paper presents an experimental study of the benefits of soft biometric labels as ancillary information based on the description of human physical features to improve challenging person recognition scenarios at a distance. In addition, we analyze the available soft biometric information in scenarios of varying distance between camera and subject. Experimental results based on the Southampton multibiometric tunnel database show that the use of soft biometric traits is able to improve the performance of face recognition based on sparse representation on real and ideal scenarios by adaptive fusion rules.

Biometric quality: a review of fingerprint, iris, and face

Abstract: Biometric systems encounter variability in data that influence capture, treatment, and u-sage of a biometric sample. It is imperative to first analyze the data and incorporate this understanding within the recognition system, making assessment of biometric quality an important aspect of biometrics. Though several interpretations and definitions of quality exist, sometimes of a conflicting nature, a holistic definition of quality is indistinct. This paper presents a survey of different concepts and interpretations of biometric quality so that a clear picture of the current state and future directions can be presented. Several factors that cause different types of degradations of biometric samples, including image features that attribute to the effects of these degradations, are discussed. Evaluation schemes are presented to test the performance of quality metrics for various applications. A survey of the features, strengths, and limitations of existing quality assessment techniques in fingerprint, iris, and face biometric are also presented. Finally, a representative set of quality metrics from these three modalities are evaluated on a multimodal database consisting of 2D images, to understand their behavior with respect to match scores obtained from the state-of-the-art recognition systems. The analysis of the characteristic function of quality and match scores shows that a careful selection of complimentary set of quality metrics can provide more benefit to various applications of biometric quality.

On-line signature verification using vertical signature partitioning

Abstract: In this paper we propose a new approach to identity verification based on the analysis of the dynamic signature. Considered problem seems to be particularly important in terms of biometrics. Effectiveness of signature verification significantly increases when dynamic characteristics of the signature are considered (e.g. velocity, pen pressure, etc.). These characteristics are individual for each user and difficult to forge. The effectiveness of the verification on the basis of an analysis of the dynamics of the signature can be further improved. A well-known way is to consider the characteristics of the signature in the sections called partitions. In this paper we propose a new method for identity verification which uses partitioning. Partitions represent time moments of signing of the user. In the classification process the partitions, in which the user created more stable reference signatures during acquisition phase, are more important. Other important features of our method are: using capabilities of fuzzy set theory and development on the basis of them the flexible neuro-fuzzy systems and interpretable classification system for final signature classification. In this paper we have included the simulation results for the two currently available databases of dynamic signatures: free SVC2004 and commercial BioSecure database.

Mobile signature verification: feature robustness and performance comparison

Abstract: In this study, the effects of using handheld devices on the performance of automatic signature verification systems are studied. The authors compare the discriminative power of global and local signature features between mobile devices and pen tablets, which are the prevalent acquisition device in the research literature. Individual feature discriminant ratios and feature selection techniques are used for comparison. Experiments are conducted on standard signature benchmark databases (BioSecure database) and a state-of-the-art device (Samsung Galaxy Note). Results show a decrease in the feature discriminative power and a higher verification error rate on handheld devices. It is found that one of the main causes of performance degradation on handheld devices is the absence of pen-up trajectory information (i.e. data acquired when the pen tip is not in contact with the writing surface).

Visual place recognition using HMM sequence matching

Abstract: Visual place recognition and loop closure is critical for the global accuracy of visual Simultaneous Localization and Mapping (SLAM) systems. We present a place recognition algorithm which operates by matching local query image sequences to a database of image sequences. To match sequences, we calculate a matrix of low-resolution, contrast-enhanced image similarity probability values. The optimal sequence alignment, which can be viewed as a discontinuous path through the matrix, is found using a Hidden Markov Model (HMM) framework reminiscent of Dynamic Time Warping from speech recognition. The state transitions enforce local velocity constraints and the most likely path sequence is recovered efficiently using the Viterbi algorithm. A rank reduction on the similarity probability matrix is used to provide additional robustness in challenging conditions when scoring sequence matches. We evaluate our approach on seven outdoor vision datasets and show improved precision-recall performance against the recently published seqSLAM algorithm.

System and methods for automated plant asset failure detection

Abstract: A system for performing failure signature recognition training for at least one unit of equipment. The system includes a memory and a processor coupled to the memory. The processor is configured by computer code to receive sensor data relating to the unit of equipment and to receive failure information relating to equipment failures. The processor is further configured to analyze the sensor data in view of the failure information in order to develop at least one learning agent for performing failure signature recognition with respect to the at least one unit of equipment.

Embedded System for Biometric Online Signature Verification

Abstract: This paper describes the implementation on field-programmable gate arrays (FPGAs) of an embedded system for online signature verification. The recognition algorithm mainly consists of three stages. First, an initial preprocessing is applied on the captured signature, removing noise and normalizing information related to horizontal and vertical positions. Afterwards, a dynamic time warping algorithm is used to align this processed signature with its template previously stored in a database. Finally, a set of features are extracted and passed through a Gaussian Mixture Model, which reveals the degree of similarity between both signatures. The algorithm was tested using a public database of 100 users, obtaining high recognition rates for both genuine and forgery signatures. The implemented system consists of a vector floating-point unit (VFPU), specifically designed for accelerating the floating-point computations involved in this biometric modality. Moreover, the proposed architecture also includes a microprocessor, which interacts with the VFPU, and executes by software the rest of the online signature verification process. The designed system is capable of finishing a complete verification in less than 68 ms with a clock rated at 40 MHz. Experimental results show that the number of clock cycles is accelerated by a factor of ×4.8 and ×11.1, when compared with systems based on ARM Cortex-A8 and when substituting the VFPU by the Floating-Point Unit provided by Xilinx, respectively.

Performance evaluation of handwritten signature recognition in mobile environments

Abstract: The utilisation of biometrics in mobile scenarios is increasing remarkably. At the same time, handwritten signature recognition is one of the modalities with highest potential of use for those applications where customers are used to sign in those traditional processes. However, several improvements have to be made in order to reach acceptable levels of performance, reliability and interoperability. The evaluation carried out in this study contributes with multiple results obtained from 43 users signing 60 times, divided in three sessions, in eight different capture devices, being six of them mobile devices and the other two digitisers specially made for signing and used as a baseline. At each session, a total of 20 signatures per user are captured by each device, so that the evaluation here reported a total of 20 640 signatures, stored in ISO/IEC 19794–7 format. The algorithm applied is a DTW-based one, particularly modified for mobile environments. The results analysed include inter-operability, visual feedback and modality tests. One of the big challenges of this research was to discover if the handwritten signature modality in mobile devices should be split into two different modalities, one for those cases when the signature is performed with a stylus, and another when the fingertip is used for signing. Many relevant conclusions have been collected and, over all, multiple improvements have been reached contributing to future deployments of biometrics in mobile environments.

A new approach to signature recognition using the fuzzy method

Abstract: The paper presents a new fuzzy approach to off-line handwritten signature recognition. The solution is based on characteristic feature extraction. After finding signature's center of gravity a number of lines are drawn through it at different angles. Cross points of generated lines and signature sample, which are further grouped and sorted, are treated as the set of features. On the basis of such structures, obtained from a chosen number of learning samples, a fuzzy model is created, called the fuzzy signature. During a verification phase the level of conformity of an input sample and the fuzzy signature is calculated. The extension in feature extraction as well as proposed fuzzy model has never been employed before. It needs to be emphasized that information stored within the verification system cannot be used to recreate the original signatures collected at the enrolment phase. The fact is particularly valuable for large databases and systems where storage safety is crucial. The solution is very flexible and allows the user to extend an intuitive structure of fuzzy sets by employing dynamic features, making the approach an on-line method. The results obtained should be still improved, similarly to the case of other known biometric systems related to signature recognition. However, the presented technique can be easily utilized in applications where FAR coefficient should be very low and is more important than FRR ratio.

A Chaotic Theorectical Approach to ECG-Based Identity Recognition [Application Notes]

Abstract: Sophisticated technologies realized from applying the idea of biometric identification are increasingly applied in the entrance security management system, private document protection, and security access control Common biometric identification involves voice, attitude, keystroke, signature, iris, face, palm or finger prints, etc Still, there are novel identification technologies based on the individual's biometric features under development [1-4]

Maximum Margin Projection Subspace Learning for Visual Data Analysis

Abstract: Visual pattern recognition from images often involves dimensionality reduction as a key step to discover a lower dimensional image data representation and obtain a more manageable problem. Contrary to what is commonly practiced today in various recognition applications where dimensionality reduction and classification are independently treated, we propose a novel dimensionality reduction method appropriately combined with a classification algorithm. The proposed method called maximum margin projection pursuit, aims to identify a low dimensional projection subspace, where samples form classes that are better discriminated, i.e., are separated with maximum margin. The proposed method is an iterative alternate optimization algorithm that computes the maximum margin projections exploiting the separating hyperplanes obtained from training a support vector machine classifier in the identified low dimensional space. Experimental results on both artificial data, as well as, on popular databases for facial expression, face and object recognition verified the superiority of the proposed method against various state-of-the-art dimensionality reduction algorithms.

Leap signature recognition using HOOF and HOT features

Abstract: With the growing need for secure authentication, there is an increasing interest in establishing newer biometric modalities that are verifiable in a fast manner with as few associated complexities as possible. In this research, we propose a new biometric modality using a Leap Motion device. The Leap signature is created by an individual in three-dimensional space in absence of any feedback from objects or surfaces. The proposed framework combines an adaptation of 3D Histogram of Oriented Optical Flow and a new feature descriptor, termed as Histogram of Oriented Trajectories. Experiments are performed on the IIITD Leap Signature Database, which consists of 900 samples from 60 subjects. The results are combined with a four-patch local binary pattern based face verification algorithm. An accuracy of over 91% is achieved on this database, with rate of successful spoofing attempts being approximately 1.4%.

Feature extraction and classification by machine learning methods for biometric recognition of face and iris

Abstract: Biometric recognition became an integral part of our living This paper deals with machine learning methods for recognition of humans based on face and iris biometrics The main intention of machine learning area is to reach a state when machines (computers) are able to respond without humans explicitly programming them This area is closely related to artificial intelligence, knowledge discovery, data mining and neurocomputing We present relevant machine learning methods with main focus on neural networks Some aspects of theory of neural networks are addressed such as visualization of processes in neural networks, internal representations of input data as a basis for new feature extraction methods and their applications to image compression and classification Machine learning methods can be efficiently used for feature extraction and classification and therefore are directly applicable to biometric systems Biometrics deals with the recognition of people based on physiological and behavioral characteristics Biometric recognition uses automated methods for recognition and this is why it is closely related to machine learning Face recognition is discussed in this presentation — it covers the aspects of face detection, detection of facial features, classification in face recognition systems, state-of-the-art in biometric face recognition, face recognition in controlled and uncontrolled conditions and single-sample problem in face recognition Iris recognition is analyzed from the point of view of state-of-the art in iris recognition, 2D Gabor wavelets, use of convolution kernels and possibilities for the design of new kernels Software and hardware implementations of face and iris recognition systems are discussed and an implementation of a multimodal interface (face and iris part of a system) is presented Also a contribution of Machine Learning Group working at FEI SUT Bratislava (http://wwwuimelfstubask/kaivt/MLgroup) to this research area is shown

A Novel Approach of Bangla Handwritten Text Recognition Using HMM

Abstract: This paper presents a novel approach for offline Bangla (Bengali) handwritten word recognition by Hidden Markov Model (HMM). Due to the presence of complex features such as headline, vowels, modifiers, etc., character segmentation in Bangla script is not easy. Also, the position of vowels and compound characters make the segmentation task of words into characters very complex. To take care of these problems we propose a novel method considering a zone-wise break up of words and next perform HMM based recognition. In particular, the word image is segmented into 3 zones, upper, middle and lower, respectively. The components in middle zone are modeled using HMM. By this zone segmentation approach we reduce the number of distinct component classes compared to total number of classes in Bangla character set. Once the middle zone portion is recognized, HMM based forced alignment is applied in this zone to mark the boundaries of individual components. The segmentation paths are extended later to other zones. Next, the residue components, if any, in upper and lower zones in their respective boundary are combined to achieve the final word level recognition. We have performed a preliminary experiment on a dataset of 10,120 Bangla handwritten words and found that the proposed approach outperforms the custom way of HMM based recognition.

A Review on Biometric Recognition

Abstract: Biometrics introduce to the recognition of human by their characteristics. It is used to identify individuals in groups. Traditional methods of identification involving passwords and PIN numbers, this new technique of identification is preferred over them. Biometric systems are divided on the basis of medium used for authentication. Face Recognition, Iris Recognition, Palm Recognition; Voice Recognition, Fingerprint Recognition, ECG signal based recognition methods are used for identification. Different techniques are used to extract the features for recognition. This paper gives a literature survey on Biometric Identification system so that new researchers do not find the difficulty for obtaining the information.

Human recognition system for outdoor videos using Hidden Markov model

Abstract: Human recognition is an essential requirement for human-centric surveillance, activity recognition, gait recognition etc. Inaccurate recognition of humans in such applications may leads to false alarm and unnecessary computation. In the proposed work a robust background modeling algorithm using fuzzy logic is used to detect foreground objects. Three distinct features are extracted from the contours of detected objects. An unique aggregated feature vector is formed using a fuzzy inference system by aggregating three feature vectors. To minimize computation in recognition using Hidden Markov model (HMM), the length of final feature vector is reduced using vector quantization. The proposed method is explained using five basic phases; background modeling and foreground object detection, features extraction, aggregated feature vector calculation, vector quantization, and recognition using Hidden Markov model.

Offline handwritten signature verification system using a supervised neural network approach

Abstract: Signatures are imperative biometric attributes of humans that have long been used for authorization purposes. Most organizations primarily focus on the visual appearance of the signature for verification purposes. Many documents, such as forms, contracts, bank cheques, and credit card transactions require the signing of a signature. Therefore, it is of upmost importance to be able to recognize signatures accurately, effortlessly, and in a timely manner. In this work, an artificial neural network based on the well-known Back-propagation algorithm is used for recognition and verification. To test the performance of the system, the False Reject Rate, the False Accept Rate, and the Equal Error Rate (EER) are calculated. The system was tested with 400 test signature samples, which include genuine and forged signatures of twenty individuals. The aim of this work is to limit the computer singularity in deciding whether the signature is forged or not, and to allow the signature verification personnel to participate in the deciding process through adding a label which indicates the amount of similarity between the signature which we want to recognize and the original signature. This approach allows judging the signature accuracy, and achieving more effective results.

Multimodal biometric systems

Abstract: Biometric is a unique, measurable physiological or behavioral characteristic of a person. The biometric system is one such that can provide accurate and reliable scheme for person verification and authentication. Unimodal biometric systems have to contend with a variety of problems such as noisy data, intra-class variations, restricted degrees of freedom, non-universality, spoof attacks, and unacceptable error rates. Several of these problems can be addressed by deploying multimodal biometric systems that combine two or more biometric modalities. By combining multiple sources of information, these systems improve matching performance, increase population coverage, deter spoofing, and facilitate indexing. Different scenarios and fusion levels are possible and different integration strategies can be adopted to consolidate information in multimodal systems.

Generation of Enhanced Synthetic Off-Line Signatures Based on Real On-Line Data

Abstract: One of the main challenges of off-line signature verification is the absence of large databases. A possible alternative to overcome this problem is the generation of fully synthetic signature databases, not subject to legal or privacy concerns. In this paper we propose several approaches to the synthesis of off-line enhanced signatures from real dynamic information. These synthetic samples show a performance very similar to the one offered by real signatures, even increasing their discriminative power under the skilled forgeries scenario, one of the biggest challenges of handwriting recognition. Furthermore, the feasibility of synthetically increasing the enrolment sets is analysed, showing promising results.

Intelligent handwriting Thai Signature Recognition System based on artificial neuron network

Abstract: This paper proposes an Intelligent Handwriting Thai Signature Recognition System base on Multilayer Perceptron and Radial Basis Network. The proposed system compose of three main processes, i.e. image pre-processing, feature extraction and Thai signature recognition. In the recognition processes the neural network is used into two stage. First, Multilayer Perceptron (MLP) and Radial Basis Function (RBF) is used to learning handwriting Thai signature and then the trained network will be used for recognizing. Later, RBF is used to decision in final stage. There are 600 images from 10 writers in this experiment then the experimental results show that the proposed method yielded the satisfied results.

Recent Advances in Offline Signature Identification

Abstract: In recent years, biometric-based authentication systems have been widely used in many applications which require reliable identification scheme. Among others, handwritten signature is one of the most interesting biometric means, that is being considered with renewed interest. This paper presents some of the most relevant advances in the field of offline signature identification and highlights some directions for further research.

Multimodal Biometric Using a Hierarchical Fusion of a Person's Face, Voice, and Online Signature

Abstract: Biometric performance improvement is a challenging task In this paper, a hierarchical strategy fusion based on multimodal biometric system is presented This strategy relies on a combination of several biometric traits using a multi-level biometric fusion hierarchy The multi-level biometric fusion includes a pre-classification fusion with optimal feature selection and a post-classification fusion that is based on the similarity of the maximum of matching scores The proposed solution enhances biometric recognition performances based on suitable feature selection and reduction, such as principal component analysis (PCA) and linear discriminant analysis (LDA), as much as not all of the feature vectors components support the performance improvement degree Keywords—Hierarchical Fusion, LDA, Multimodal Biometric Fusion, PCA

Biometric person authentication: A review

Abstract: A process which ensures person authentication information by processing unique feature vectors, which were fetched from human inimitable characteristics, is known as unimodal biometric system (UBS). Though these systems provide a very good result, they fail in certain conditions such as noisy acquisition, missing out of unique human characteristics etc. There is need for robust biometric system. Current smart electronic devices capture and process enormous amount of data and execute results in less time. This paper presents past research and development in the field of biometric technology. It also describes major technological perspective and fundamental progress in unimodal and multimodal biometric system. Also, it reviews the modalities of biometric system, biometric classification methods, past experimental results, merits and demerits of unimodal system. It describes the integration scenario of multimodal biometrics. The objective of this review paper is to summarize past and current trends, and it conclude with the decision on future direction for developing proficiency in the field of person authentication biometric technology.

Online Signature Verification Based on Kolmogorov-Smirnov Distribution Distance

Abstract: Online signature verification methods examine the dynamics of the handwriting process to decide whether a signature is probably genuine or forged. Most of the previously proposed methods for online signature verification apply Neural Networks, Dynamic Time Warping, or Hidden Markov Model for classification and they consider several aspects, like planar coordinates, pressure, velocity, and acceleration with respect to time. Here we apply a non-parametric statistical test for a comparison of features and the verification of signatures.

Offline Signature Verification using Grid based and Centroid based Approach

Abstract: Now a day’s Signature verification is one of the most important features for checking the authenticity of a person. There are many security checking parameters like pin code, password, finger print checking but signature recognition is the most popular because it is quite accurate and cost efficient too. On the other hand one doesn’t have to remember the authentication key like pin code or password. The signature of a genuine signer stays almost constant. But there may be little difference between well practiced forgeries and the genuine signer. It is required to distinguish these differences. This paper presents grid based, contour based and area based approach for signature verification. Intersecting points and centroids of two equal half of the signature is being calculated and then those centroids are connected with a straight line and the angles of these intersecting points with respect to the centroids connecting lines are calculated. General Terms Signature Verification, Grid based approach, Centroid based approach and contour based approach.

Cognitive Inspired Model to Generate Duplicated Static Signature Images

Abstract: The handwriting signature is one of the most popular behavioral biometric traits for person recognition. Such recognition systems capture the personal signing behaviour and its variability based on a limited number of enrolled signatures. In this paper a cognitive inspired model based on motor equivalence theory is developed to duplicate off-line signatures from one real on-line seed. This model achieves duplicated signatures with a natural variability. It is validated with an off-line signature verifier based on texture features and a SVM classifier. The results manifest the complementarity of the duplicated signatures and the utility of the model.

Towards Arabic Handwritten Word Recognition via Probabilistic Graphical Models

Abstract: —In this work, we propose a novel system for the recognition of handwritten Arabic words. It is evolved based on horizontal-vertical Hidden Markov Model and Dynamic Bayesian Network Model. Our strategy consists of looking for various HMM architectures and selecting those which provide the best recognition performance. Experiments on handwritten Arabic words from IFN/ENIT strongly support the feasibility of the proposed approach. The recognition rates achieve 92.19% with horizontal-vertical Hidden Markov Model and 88.82% with a Dynamic Bayesian Network.

Face recognition using PCA and different distance classifiers

Abstract: A facial recognition system is a computer application for automatically identifying or verifying a person from a digital image or a video frame from a video source. One of the way is to do this is by comparing selected facial features from the image and a facial database. It is typically used in security systems and can be compared to other biometrics such as fingerprint or eye iris recognition systems. In this paper we focus on 3-D facial recognition system and biometric facial recognition system. We do critics on facial recognition system giving effectiveness and weaknesses. This paper also introduces scope of recognition system in India. Face recognition has received substantial attention from researchers in biometrics, pattern recognition field and computer vision communities. Face recognition can be applied in Security measure at Air ports, Passport verification, Criminals list verification in police department, Visa processing, Verification of Electoral identification and Card Security measure at ATM's. Principal Component Analysis (PCA) is a technique among the most common feature extraction techniques used in Face Recognition. In this paper, a face recognition system for personal identification and verification using Principal Component Analysis with different distance classifiers is proposed. The test results in the ORL face database produces interesting results from the point of view of recognition success, rate, and robustness of the face recognition algorithm. Different classifiers were used to match the image of a person to a class (a subject) obtained from the training data. These classifiers are: the City-Block Distance Classifier, the Euclidian distance classifier, the Squared Euclidian Distance Classifier, and