Showing papers on "Signature recognition published in 2005"

Correlation Pattern Recognition

Abstract: Correlation is a robust and general technique for pattern recognition and is used in many applications, such as automatic target recognition, biometric recognition and optical character recognition The design, analysis and use of correlation pattern recognition algorithms requires background information, including linear systems theory, random variables and processes, matrix/vector methods, detection and estimation theory, digital signal processing and optical processing This 2005 book provides a needed review of this diverse background material and develops the signal processing theory, the pattern recognition metrics, and the practical application know-how from basic premises It shows both digital and optical implementations It also contains technology presented by the team that developed it and includes case studies of significant interest, such as face and fingerprint recognition Suitable for graduate students taking courses in pattern recognition theory, whilst reaching technical levels of interest to the professional practitioner

Biometric based user authentication and data encryption

Abstract: Biometric parameters acquired from human forces, voices, fingerprints, and irises are used for user authentication and access control. Because the biometric parameters are continuous and vary from one reading to the next, syndrome codes are applied to determine biometric syndrome vectors. The biometric syndrome vectors can be stored securely while tolerating an inherent variability of biometric data. The stored biometric syndrome vector is decoded during user authentication using biometric parameters acquired at that time. The syndrome codes can also be used to encrypt and decrypt data.

Local and global feature selection for on-line signature verification

Abstract: In this paper we propose a methodology for selecting the most discriminative features in a set for online signature verification. We expose the difference in the definition of class between signature verification and other pattern recognition tasks, and extend the classical Fisher ratio to make it more robust to the small sample sizes typically found when dealing with global features and client enrollment time constraints for signature verification systems. We apply our methodology to global and local features extracted from a 50-users database, and find that our criterion agrees better with classifier error rates for local features than for global features. We discuss the possibility of performing feature selection without having forgery data available.

Use of exterior contours and shape features in off-line signature verification

Abstract: An approach to off-line signature verification, one with an on-line flavor, is described. A sequence of data is obtained by tracing the exterior contour of the signature which allows the application of string-matching algorithms. The upper and lower contours of the signature are first determined by ignoring small gaps between signature components. The contours are combined into a single sequence so as to define a pseudo-writing path. To match two signatures a non-linear normalization method, viz., dynamic time warping, is applied to segment them into curves. Shape descriptors based on Zernike moments are extracted as features from each segment. A harmonic distance is used for measuring signature similarity. Performance is significantly better than that of a word-shape based signature verification method. When the two methods are combined, the overall performance is significantly better than either method alone. With a database of 1320 genuines and 1320 forgeries the combination method has an accuracy of 95% (with 20% rejection) which is comparable to that of on-line systems.

Online Context Recognition in Multisensor Systems using Dynamic Time Warping

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

Speech recognition with support vector machines in a hybrid system.

Abstract: While the temporal dynamics of speech can be represented very efficiently by Hidden Markov Models (HMMs), the classification of speech into single speech units (phonemes) is usually done with Gaussian mixture models which do not discriminate well. Here, we use Support Vector Machines (SVMs) for classification by integrating this method in a HMM-based speech recognition system. In this hybrid SVM/HMM system we translate the outputs of the SVM classifiers into conditional probabilities and use them as emission probabilities in a HMM-based decoder. SVMs are very appealing due to their association with statistical learning theory. They have already shown very good classification results in other fields of pattern recognition. We train and test our hybrid system on the DARPA Resource Management (RM1) corpus. Our results show better performance than HMM-based decoder using Gaussian mixtures.

Signature recognition state-of-the-art

Abstract: A summarization of one of the most successful behavioral biometric recognition methods: signature recognition. Probably this is one of the oldest biometric recognition methods, with high legal acceptance. Technological advances have made possible new perspectives for signature recognition, by means of capturing devices which provide more than the simple signature image: pressure, acceleration, etc., making it even more difficult to forge a signature.

System and method for capturing and applying a legal signature to documents over a network

Abstract: An electronic signature capture system and process meets the legal requirements of a valid electronic signature while also providing electronically signed documents that have the appearance, and thus equivalent acceptability, of a traditional pen-and-ink signature. The documents can be signed using a mouse, a stylus, a touch screen, a graphics tablet, or other suitable input device to draw a signature analogue on the screen similar to signing a paper document with a pen. A fingerprint image, retinal scan image, or other similar biometric input may be captured in addition to or instead of a signature. The signature analogue is saved and linked to a particular user and to particular documents. The signature analogue may be combined with the document in a composite image file, or the signature analogue may be applied dynamically to appropriate document components to assemble an executed document as needed.

Feature extraction apparatus and method and pattern recognition apparatus and method

Abstract: It is intended to increase the recognition rate in speech recognition and image recognition. An observation vector as input data, which represents a certain point in the observation vector space, is mapped to a distribution having a spread in the feature vector space, and a feature distribution parameter representing the distribution is determined. Pattern recognition of the input data is performed based on the feature distribution parameter.

Method and system of transitive matching for object recognition, in particular for biometric searches

Abstract: A method and system of transitivity matching suitable for object recognition, and in particular biometric recognition such as face or fingerprint recognition, is provided. The invention provides a means for pre-encoding object match information from each set of raw object sample data or scores from an underlying recognition algorithm. A sample space is constructed and the raw scores are mapped into that space. Preferably, a recognition space is also constructed and the sample space scores are further mapped into the recognition space. Each space may be truncated to remove dispensable modes of that space. The distance between object data samples encoded in the sample space or the recognition space can be more rapidly determined, and the encoded data samples are also significantly compressed, compared to the raw scores.

3D face recognition for biometric applications

Abstract: Face recognition (FR) is the preferred mode of identity recognition by humans: It is natural, robust and unintrusive. However, automatic FR techniques have failed to match up to expectations: Variations in pose, illumination and expression limit the performance of 2D FR techniques. In recent years, 3D FR has shown promise to overcome these challanges. With the availability of cheaper acquisition methods, 3D face recognition can be a way out of these problems, both as a stand-alone method, or as a supplement to 2D face recognition. We review the relevant work on 3D face recognition here, and discuss merits of different representations and recognition algorithms.

Viewpoint invariant sign language recognition

Abstract: Sign language is the primary modality of communication among deaf and mute society all over the world. This paper proposes a viewpoint independent method for sign recognition. Considering that two sequences of the same sign can be roughly considered as the input of a stereo vision system after time-warping, and the fundamental matrix associated with two views should be unique, we can convert the temporal-spatial recognition task as a verification task within a stereo vision framework. After time-warping of the input sequences, the proposed framework can reach both temporal and viewpoint invariance. We demonstrate the efficiency of the proposed framework by recognizing a vocabulary of 100 words of Chinese sign language. The recognition rate is up to 97% at rank 3. Furthermore, the proposed framework can be easily extended to other recognition tasks, such as gait recognition and lip-reading recognition.

On-line signature verification with two-stage statistical models

Abstract: Signature verification is a challenging task, because only a small set of genuine samples can be acquired and usually no forgeries are available in real application. In this paper, we propose a new two-stage statistical system for automatic on-line signature verification. Our system is composed of a simplified GMM model for global signature features, and a discrete HMM model for local signature features. To be practical, we introduce specific simplification strategies for model building and training. Our system requires only 5 genuine samples for new users and relies on only 3 global parameters for quick and efficient system tuning. Experiments are conducted to verify the effectiveness of our system.

Robust DTW-based recognition algorithm for hand-held consumer devices

Abstract: This paper presents a new robust dynamic programming (DP)-based recognition algorithm that is quite suitable for menu-driven recognition applications with small vocabulary size (typically less than 50). When compared to the conventional dynamic time warping (DTW)-based recognizer, the proposed algorithm shows significantly improved recognition accuracy in speaker-independent cases. In addition, when compared to the conventional hidden Markov model (HMM)-based recognizer, the proposed algorithm requires smaller computational amount and parameter file size, while maintaining almost the same recognition rate for command recognition applications with small vocabulary size in hand-held consumer devices.

Cryptographic keys from dynamic hand-signatures with biometric secrecy preservation and replaceability

Abstract: We propose a method of extracting cryptographic key from dynamic handwritten signatures that does not require storage of the biometric template or any statistical information that could be used to reconstruct the biometric data. Also, the keys produced are not permanently linked to the biometric hence, allowing them to be replaced in the event of key compromise. This is achieved by incorporating randomness which provides high-entropy to the naturally low-entropy biometric key using iterative inner-product method as in Goh-Ngo, and modified multiple-bit discretization that deters guessing from key statistics. Our proposed methodology follows the design principles of block ciphers to result in unpredictable key space and secure construction.

On-line and off-line signature verification using relative slope algorithm

Abstract: The paper presents a novel relative slope based algorithm for an on-line and off-line signature verification system capable of effectively establishing an individual's identify based solely on their handwriting characteristics. Current technologies in signature verification systems use various algorithms for feature point extraction, regression approach, Markov method, split and merge and genetic algorithms. We propose a slope based model, in which the input signature is divided into many segments using an optimized HMM method; then, the slope of every segment is calculated with respect to its previous segment, obtained after normalization of the signature. This feature of each segment is stored along with the two tier time metric information, which ensures lesser overhead with better performance while processing.

On-line signature verification using Tablet PC

Abstract: On-line signature verification for Tablet PC devices is studied. The on-line signature verification algorithm presented by the authors at the First International Signature Verification Competition (SVC 2004) is adapted to work in Tablet PC environments. An example prototype of securing access and securing document application using this Tablet PC system is also reported. Two different commercial Tablet PCs are evaluated, including information of interest for signature verification systems such as sampling and pressure statistics. Authentication performance experiments are reported considering both random and skilled forgeries by using a new database with over 3000 signatures.

Biometric signatures and identification through the use of projective invariants

Abstract: Techniques, systems and methods for obtaining biometric signatures and identification are described. Broadly stated, embodiments of the present invention utilize specified geometric principles t provide means for accurate biometric identification using projective invariant features of a subregion of the human body. The present invention provides a means for computing biometric signatures and identification that are projective invariant and hence are not impacted by the viewing angle of the subregion of the human body containing the biometric data. This novel invention removes the restriction, often implicit in the previous work, of the imaging or sensing system being in a fixed repeatable (and generally orthogonal) viewing position. This invention can be applied across a wide range of biometrics, although it is most easily applicable to features that are approximately co-planar. A plurality of such projective invariant features can be used to define a biometric signature to either verify an individual’s identity, or recognize an individual from a database of already known persons.

Online and offline character recognition using alignment to prototypes

Abstract: Nearest neighbor classifiers are simple to implement, yet they can model complex non-parametric distributions, and provide state-of-the-art recognition accuracy in OCR databases. At the same time, they may be too slow for practical character recognition, especially when they rely on similarity measures that require computationally expensive pair-wise alignments between characters. This paper proposes an efficient method for computing an approximate similarity score between two characters based on their exact alignment to a small number of prototypes. The proposed method is applied to both online and offline character recognition, where similarity is based on widely used and computationally expensive alignment methods, i.e., dynamic time warping and the Hungarian method respectively. In both cases significant recognition speedup is obtained at the expense of only a minor increase in recognition error.

Off-line handwritten signature verification with inflections feature

Abstract: In this paper, an off-line handwritten signature verification system is proposed. Different scale wavelet transforms are used in the curvature signature signals transformation. After analysis, proper scale is selected. We extract the zero-crossings points and take it as the inflections of the signature. Then the signature curves are divided into several parts, i.e. the strokes, according to these inflections. The distance between two corresponding strokes can be measured with dynamic time warping algorithm. In the end, the training algorithm of the signature verification system and the verification method of the signatures are also introduced. Experimental results verify the effectiveness of this method.

Ensemble methods for handwritten text line recognition systems

Abstract: This paper investigates the generation and use of classifier ensembles for offline handwritten text recognition. The ensembles are derived from the integration of a language model in the hidden Markov model based recognition system. The word sequences output by the ensemble members are aligned and combined according to the ROVER framework. The addressed environment is extreme because of the existence of a large number of word classes. Moreover, the recognisers do not produce single output classes but sequences of classes. Experiments conducted on the IAM database show that the ensemble methods are able to produce statistically significant improvements in the word level accuracy when compared to the base recogniser.

Signature recognition and verification with artificial neural network using moment invariant method

Abstract: In this paper, we present off-line signature recognition and verification system which is based on image processing, moment invariant method and ANN. Two separate sequential neural networks are designed; one for signature recognition, and another for verification (i.e. for detecting forgery). Verification network parameters which are produced individually for every signature are controlled by a recognition network. The System overall performs is enough to signature recognition and verification.

Wavelet Transform Based Global Features for Online Signature Recognition

Abstract: This paper presents an efficient algorithm for an online signature verification system that is based on the extraction of global features from the spatial coordinates obtained during the online acquisition of a signature using one dimensional wavelet transform. A k-NN classifier is used for classification purposes. Low error rates obtained for both random and skilled forgeries datasets illustrate the feasibility of the algorithm for an online signature verification system

Enhancing training data for handwriting recognition of whiteboard notes with samples from a different database

Abstract: Recognition of unconstrained handwritten text is still a challenge In this paper we consider a new problem, which is the recognition of notes written on a whiteboard Our recognizer is based on hidden Markov models (HMMs) As it is difficult to acquire sufficient amounts of training data for the HMMs we propose two strategies for enlarging the training set Both strategies are based on an existing database of offline handwritten text, which includes handwriting samples different from whiteboard data The two proposed strategies are MAP adaptation and merging of training sets With these methods we can achieve improvements of the word recognition rate of up to 57%

Multiple Handwritten Text Line Recognition Systems Derived from Specific Integration of a Language Model

Abstract: This paper investigates the generation and use of multiple recognition results to improve the performance of an offline handwritten text line recognition system. Multiple recognition results are created by specific integration of a language model in the hidden Markov model based recognition system. The ROVER algorithm is applied to combine the multiple results. Experiments conducted on the IAM database show that the proposed system is able to produce statistically significant improvements in the recognition rate compared to the original system.

Online authorization using biometric and digital signature schemes

Abstract: A method, system, mobile terminal and computer program product for authenticating a user's signature is provided. In general, the authentication scheme introduced combines the benefits of both biometric and digital signature schemes by projecting a sequence of predefined images onto a surface, enabling the user to sign, or otherwise write, across the projected images, capturing this signing process in the form, for example, of a video clip, applying the user's digital signature to the clip of his/her biometric signature, and then using the biometric and digital signatures to authenticate the user.