Showing papers on "Signature recognition published in 2003"

Comparison and combination of ear and face images in appearance-based biometrics

Abstract: Researchers have suggested that the ear may have advantages over the face for biometric recognition. Our previous experiments with ear and face recognition, using the standard principal component analysis approach, showed lower recognition performance using ear images. We report results of similar experiments on larger data sets that are more rigorously controlled for relative quality of face and ear images. We find that recognition performance is not significantly different between the face and the ear, for example, 70.5 percent versus 71.6 percent, respectively, in one experiment. We also find that multimodal recognition using both the ear and face results in statistically significant improvement over either individual biometric, for example, 90.9 percent in the analogous experiment.

Method and apparatus for electro-biometric identity recognition

Abstract: A method and apparatus for electro-biometric identity recognition or verification, producing and storing a first biometric signature that identifies a specific individual by forming the difference between a representation of the heartbeat pattern of the specific individual and a stored representation of common features of the heartbeat patterns of a plurality of individuals; after the producing step, obtaining a representation of the heartbeat pattern of a selected individual and producing a second biometric signature by forming the difference between the heartbeat pattern of the selected individual and the stored representation of common features of the heartbeat patterns of the plurality of individuals; and comparing the second biometric signature with the first biometric signature to determine whether the selected individual is the specific individual.

Identity verification system

Abstract: The present invention is a security system that utilizes an identity verification system having a biometrics component, such as but not limited to a face, fingerprint, or iris recognition system. The system connects a biometric data entry device such as a standard analogue or digital camera to a communication control device which captures, compresses and digitizes the biometric data as well as converts data from data input devices and sends the compressed and digitized biometric data along with the data from a data input device to a central processing unit for processing by a biometric recognition system and comparison to stored biometric data.

Visual identification by signature tracking

Abstract: We propose a new camera-based biometric: visual signature identification. We discuss the importance of the parameterization of the signatures in order to achieve good classification results, independently of variations in the position of the camera with respect to the writing surface. We show that affine arc-length parameterization performs better than conventional time and Euclidean arc-length ones. We find that the system verification performance is better than 4 percent error on skilled forgeries and 1 percent error on random forgeries, and that its recognition performance is better than 1 percent error rate, comparable to the best camera-based biometrics.

Generation of synthetic training data for an HMM-based handwriting recognition system

Abstract: A perturbation model for generating synthetic text lines from existing cursively handwritten lines of text produced by human writers is presented. Our purpose is to improve the performance of an HMM-based off-line cursive handwriting recognition system by providing it with additional synthetic training data. Two kinds of perturbations are applied, geometrical transformations and thinning/thickening operations. The proposed perturbation model is evaluated under different experimental conditions.

Automatic Person Verification Using Speech and Face Information

Abstract: Interest in biometric based identification and verification systems has increased considerably over the last decade. As an example, the shortcomings of security systems based on passwords can be addressed through the supplemental use of biometric systems based on speech signals, face images or fingerprints. Biometric recognition can also be applied to other areas, such as passport control (immigration checkpoints), forensic work (to determine whether a biometric sample belongs to a suspect) and law enforcement applications (e.g. surveillance). While biometric systems based on face images and/or speech signals can be useful, their performance can degrade in the presence of challenging conditions. In face based systems this can be in the form of a change in the illumination direction and/or face pose variations. Multi-modal systems use more than one biometric at the same time. This is done for two main reasons -- to achieve better robustness and to increase discrimination power. This thesis reviews relevant backgrounds in speech and face processing, as well as information fusion. It reports research aimed at increasing the robustness of single- and multi-modal biometric identity verification systems. In particular, it addresses the illumination and pose variation problems in face recognition, as well as the challenge of effectively fusing information from multiple modalities under non-ideal conditions.

An HMM online signature verifier incorporating signature trajectories

Abstract: Authentication of individuals is rapidly becoming an important issue. On-line signature verification is one of the methods that use biometric features. This paper proposes a new HMM algorithm for on-line signature verification. After preprocessing, input signature is discretized in a polar coordinate system. This particular discretization leads to a simple procedure for assigning initial state and state transition probabilities. This paper utilizes only pen position trajectories, no other information is used which makes the algorithm simple and fast. A preliminary experiment shows that the proposed algorithm appears to be promising.

Online Handwritten Signature Verification Using Hidden Markov Models

Abstract: Most people are used to signing documents and because of this, it is a trusted and natural method for user identity verification, reducing the cost of password maintenance and decreasing the risk of eBusiness fraud. In the proposed system, identity is securely verified and an authentic electronic signature is created using biometric dynamic signature verification. Shape, speed, stroke order, off-tablet motion, pen pressure and timing information are captured and analyzed during the real-time act of signing the handwritten signature. The captured values are unique to an individual and virtually impossible to duplicate. This paper presents a research of various HMM based techniques for signature verification. Different topologies are compared in order to obtain an optimized high performance signature verification system and signal normalization preprocessing makes the system robust with respect to writer variability.

Biometric identity verification using on-line & off-line signature verification

Abstract: Biometrics is the utilization of biological characteristics (face, iris, fingerprint) or behavioral traits (signature, voice) for identity verification of an individual. Biometric authentication is gaining popularity as a more trustable alternative to password-based security systems as it is relatively hard to be forgotten, stolen, or guessed. Signature is a behavioral biometric: it is not based on the physical properties, such as fingerprint or face, of the individual, but behavioral ones. As such, one's signature may change over time and it is not nearly as unique or difficult to forge as iris patterns or fingerprints, however signature's widespread acceptance by the public, make it more suitable for certain lower-security authentication needs. Signature verification is split into two according to the available data in the input. Off-line signature verification takes as input the image of a signature and is useful in automatic verification of signatures found on bank checks and documents. On-line signature verification uses signatures that are captured by pressure-sensitive tablets and could be used in real time applications like credit card transactions or resource accesses. In this work we present two complete systems for on-line and off-line signature verification. During registration to either of the systems the user has to submit a number of reference signatures which are cross aligned to extract statistics describing the variation in the user's signatures. Both systems have similar verification methodology and differ only in data acquisition and feature extraction modules. A test signature's authenticity is established by first aligning it with each reference signature of the claimed user, resulting in a number of dissimilarity scores: distances to nearest, farthest and template reference signatures. In previous systems, only one of these distances, typically the distance to the nearest reference signature or the distance to a template signature, was chosen, in an ad-hoc manner, to classify the signature as genuine or forgery. Here we propose a method to utilize all of these distances, treating them as features in a two-class classification problem, using standard pattern classification techniques. The distances are first normalized, resulting in a three dimensional space where genuine and forgery signature distributions are well separated. We experimented with the Bayes classifier, Support Vector Machines, and a linear classifier used in conjunction with Principal Component Analysis, to classify a given signature into one of the two classes (forgery or genuine). Test data sets of 620 on-line and 100 off-line signatures were constructed to evaluate performances of the two systems. Since it is very difficult to obtain real forgeries, we obtained skilled forgeries which are supplied by forgers who had access to signature data to practice before forging. The online system has a 1.4% error in rejecting forgeries, while rejecting only 1.3% of genuine signatures. As an offine signature is easier to forge, the offine system's performance is lower: a 25% error in rejecting forgery signatures and 20% error in rejecting genuine signatures. The results for the online system show significant improvement over the state-of-the-art results, and the results for the offline system are comparable with the performance of experienced human examiners.

Offline recognition of large vocabulary cursive handwritten text

Abstract: This paper presents a system for the offline recognitionof cursive handwritten lines of text. The system is based oncontinuous density HMMs and Statistical Language Models.The system recognizes data produced by a single writer.No a-priori knowledge is used about the content of the textto be recognized. Changes in the experimental setup withrespect to the recognition of single words are highlighted.The results show a recognition rate of ~85% with a lexiconcontaining 50'000 words. The experiments were performedover a publicly available database.

Model length adaptation of an HMM based cursive word recognition system

Abstract: On the basis of a well accepted, HMM-based cursive script recognition system, an algorithm which automatically adapts the length of the models representing the letter writing variants is proposed. An average improvement in recognition performance of about 2.72 percent could be obtained. Two initialization methods for the algorithm have been tested, which show quite different behaviors; both prove to be useful in different application areas. To get a deeper insight into the functioning of the algorithm a method for the visualization of letter HMMs is developed. It shows the plausibility of most results, but also the limitations of the proposed method. However, these are mostly due to given restrictions of the training and recognition method of the underlying system.

Line segment distribution of sketches for Persian signature recognition

Abstract: A novel fast method for line segment extraction based on chain code representation of thinned sketches (or edge maps) is presented and exploited for Persian signature recognition. The method has a parallel nature and can be employed on parallel machines. It breaks the macro chains into several micro chains after applying shifting, smoothing and differentiating. The micro chains are then approximated by straight line segments. Length and position distributions of the extracted line segments are used to make a compact feature vector for Iranian cursive signature. The feature vector is invariant under affine transforms and can be used effectively in paperless office projects. Experimental results show fast response and accurate recognition/retrieval rate.

Signature recognition and verification with ANN

Abstract: In this paper, we present an off-line signature recognition and verification system which is based on moment invariant method and ANN. Two separate neural networks are designed; one for signature recognition, and another for verification (i.e. for detecting forgery). Both networks use a four-step process. First step is to separate the signature from its background. Second step performs normalization and digitization of the original signature. Moment invariant vectors are obtained in the third step. And the last step implements signature recognition and verification.

Perceptual Analysis of Handwritten Signatures for Biometric Authentication

Abstract: The Internet has stimulated increased activity to address key problems relating to the implementation of reliable and robust biometric identity checking. Although not always the biometric modality most readily adopted in such an environment, the handwritten signature continues to offer many advantages over some other more commonly considered biometrics. The authors address some key issues relating to the nature of the handwritten signature and, especially, the strategies used by humans in analysing signature data. Through experimental studies and an analytical investigation, the paper identifies characteristics of the signature which influence its resilience to fraudulent penetration, pointing to some important principles on which to build procedures for both automated and non-automated identity authentication.

A FPGA-based HMM for a discrete Arabic speech recognition system

Abstract: In this work we propose a speech recognition system for Arabic speech based on a hardware/software co-design implementation approach. Speech recognition is a computationally demanding task, specially the pattern matching stage. The Hidden Markov Model (HMM) is considered the most powerful modeling and matching technique in the different speech recognition tasks. Implementing the pattern matching algorithm, which is time consuming, using dedicated hardware will speed up the recognition process. In this paper, a pattern matching algorithm based on HMM is implemented using Field Programmable Gate Array (FPGA). The forward algorithm, core of matching algorithm in HMM, is analyzed and modified to be more suitable for FPGA implementation. Implementation results showed that the recognition accuracy of the modified algorithm is very close to the classical algorithm with the gain of achieving higher speed and less occupied area in the FPGA. The proposed approach is used for isolated Arabic word recognition and achieved a recognition accuracy comparable with the powerful classical recognition system.

An HMM on-line signature verification algorithm

Abstract: Authentication of individuals is rapidly becoming an important issue. On-line signature verification is one of the methods that use biometric features of individuals. This paper proposes a new HMM algorithm for on-line signature verification incorporating signature trajectories. The algorithm utilizes only pen position trajectories. No other information is used which makes the algorithm simple and fast. A Preliminary experiment was performed and the intersection of FAR and FRR was 2.78%.

A recognition and verification strategy for handwritten word recognition

Abstract: In this paper a word recognition and verification scheme based on HMMs is presented. However, the main contribution of the current work lies in the validation of such a strategy. In order to perform this task, we carried out some experiments on word recognition using a legal amount database and then we compared the results reached with other study which makes use of the same database. The experiments demonstrate the efficiency of the strategy we developed for word recognition and verification.

A classification of biometric signatures

Abstract: The domain of biometrics lacks of a systematical approach for classifying biometric signatures for biometric authentication, detection, and reaction systems. This paper presents a first approach to fill this gap. Outlining the general authentication process and analyzing the meaning of the term signature from selected sciences, a definition of the term biometric signature as (bin|n-)ary coded representation of biometric characteristics is derived. To show the suitability of the suggested definition, its role within the core processes of biometric authentication systems (enrollment, authentication, derollment) is described.

Character recognition apparatus and method

Abstract: A character recognition apparatus which performs character recognition with increased accuracy on a document image including plural languages. A re-recognition range is set based on the result of recognition using a first recognition unit, and character recognition by a second recognition unit is performed within the set range. In the re-recognition range, if a similarity of the result of re-recognition is higher than that by the first recognition unit, the result of recognition by the first recognition unit is replaced with the result of recognition by the second recognition unit.

A method of biomimetic pattern recognition for face recognition

Abstract: A new method of face recognition, based on biomimetic pattern recognition and multi-weights neuron network, had been proposed. A model for face recognition that is based on biomimetic pattern recognition had been discussed, and a new method of facial feature extraction also had been introduced. The results of experiments with BPR and k-nearest neighbor rules showed that the method based on BPR can eliminate the error recognition of the samples of the types that not be trained, the correct rate is also enhanced.

Optical character recognition for model-based object recognition applications

Abstract: This paper discusses the performance of Fourier descriptors and Hu's seven moment invariants for an Optical Character Recognition (OCR) engine developed for 3D model-based object recognition applications.

Method and apparatus for recognition of biometric data following recording from at least two directions

Abstract: A method and an apparatus for recognition of biometric data with high fraud resistance, in particular for recognition of characteristics of fingers and of faces, wherein an object is acquired by optical scanning and numerical parameters are acquired by means of digital image processing.

Biometric Security Systems: Fingerprint and Speech Technology.

Abstract: This paper deals with the design of a biometric security system based upon the fingerprint and speech technology. In the first chapter there are the biometric security systems and a concept of an integration of the both technologies introduced. Then the fingerprint technology followed by the speech technology is shortly described. There are discussed some basic principles of each of the technologies.

HMM based handwritten text recognition using biometrical data acquisition pen

Abstract: Development of new text and graphical input devices is considered to be important part of human-computer interaction by many researchers worldwide. The paper presents our experience with the text recognition methods that we have developed for a new designed electronic pen that produces signals corresponding to the movement of the pen on paper. Signals are described by a set of primitives and hidden Markov models are used for word recognition. Results of tests are discussed as well as other possible application areas of our electronic pen.