Showing papers on "Signature recognition published in 2012"

Advanced optical correlation and digital methods for pattern matching—50th anniversary of Vander Lugt matched filter

Abstract: On the verge of the 50th anniversary of Vander Lugt’s formulation for pattern matching based on matched filtering and optical correlation, we acknowledge the very intense research activity developed in the field of correlation-based pattern recognition during this period of time. The paper reviews some domains that appeared as emerging fields in the last years of the 20th century and have been developed later on in the 21st century. Such is the case of three-dimensional (3D) object recognition, biometric pattern matching, optical security and hybrid optical–digital processors. 3D object recognition is a challenging case of multidimensional image recognition because of its implications in the recognition of real-world objects independent of their perspective. Biometric recognition is essentially pattern recognition for which the personal identification is based on the authentication of a specific physiological characteristic possessed by the subject (e.g. fingerprint, face, iris, retina, and multifactor combinations). Biometric recognition often appears combined with encryption–decryption processes to secure information. The optical implementations of correlation-based pattern recognition processes still rely on the 4f-correlator, the joint transform correlator, or some of their variants. But the many applications developed in the field have been pushing the systems for a continuous improvement of their architectures and algorithms, thus leading towards merged optical–digital solutions.

Ensemble of SVM trees for multimodal emotion recognition

Abstract: In this paper we address the sentence-level multi-modal emotion recognition problem. We formulate the emotion recognition task as a multi-category classification problem and propose an innovative solution based on the automatically generated ensemble of trees with binary support vector machines (SVM) classifiers in the tree nodes. We demonstrate the efficacy of our approach by performing four-way (anger, happiness, sadness, neutral) and five-way (including excitement) emotion recognition on the University of Southern California's Interactive Emotional Motion Capture (USC-IEMOCAP) corpus using combinations of acoustic features, lexical features extracted from automatic speech recognition (ASR) output and visual features extracted from facial markers traced by a motion capture system. The experiments show that the proposed ensemble of trees of binary SVM classifiers outperforms classical multi-way SVM classification with one-vs-one voting scheme and achieves state-of-the-art results for all feature combinations.

Multimodal Cancelable Biometrics

Abstract: Multimodal biometric systems have emerged as highly successful new approach to combat problems of unimodal biometric system such as intraclass variability, interclass similarity, data quality, non-universality, and sensitivity to noise. However, one major issue pertinent to unimodal system remains. It has to do with actual biometric characteristics of users being permanent, and their number being limited. Thus, if user's biometric is compromised, it might be impossible or highly difficult to replace it in a particular system. Cancellable biometric for individual biometric has been a significantly understudied problem. The concept of cancelable biometric or cancelability is to transform a biometric data or feature into a new one so that users can change their single biometric template in a biometric security system. However, cancelability in multimodal biometric has been barely addressed at all. In this paper, we tackle the problem and present a novel solution for cancelable biometrics in multimodal system. We develop a new cancelable biometric template generation algorithm using random projection and transformation-based feature extraction and selection. Performance of the proposed algorithm is validated on multi-modal face and ear database.

Poisoning adaptive biometric systems

Abstract: Adaptive biometric recognition systems have been proposed to deal with natural changes of the clients' biometric traits due to multiple factors, like aging However, their adaptability to changes may be exploited by an attacker to compromise the stored templates, either to impersonate a specific client, or to deny access to him In this paper we show how a carefully designed attack may gradually poison the template gallery of some users, and successfully mislead a simple PCA-based face verification system that performs self-update

Integrating iris and signature traits for personal authentication using user-specific weighting.

Abstract: Biometric systems based on uni-modal traits are characterized by noisy sensor data, restricted degrees of freedom, non-universality and are susceptible to spoof attacks. Multi-modal biometric systems seek to alleviate some of these drawbacks by providing multiple evidences of the same identity. In this paper, a user-score-based weighting technique for integrating the iris and signature traits is presented. This user-specific weighting technique has proved to be an efficient and effective fusion scheme which increases the authentication accuracy rate of multi-modal biometric systems. The weights are used to indicate the importance of matching scores output by each biometrics trait. The experimental results show that our biometric system based on the integration of iris and signature traits achieve a false rejection rate (FRR) of 0.08% and a false acceptance rate (FAR) of 0.01%.

Biometric information processing apparatus, biometric information processing method, and biometric information processing computer program

Abstract: A biometric information processing apparatus includes: a biometric information acquiring unit which acquires a user's biometric information and generates a biometric input image representing the biometric information; and a processing unit. The processing unit implements: generating a first intermediate image by applying first image processing to the biometric input image; generating a second intermediate image by applying second image processing to the biometric input image; detecting, from each of the first and second intermediate images, a singular point candidate; calculating a distance between the singular point candidates detected from each of the first and second intermediate images for the same singular point contained in the biometric information; calculating a quality metric for the biometric input image based on the distance; and if the quality metric is not higher than a predefined threshold value, then prompting the user to have the user's biometric information reacquired by the biometric information acquiring unit.

Multimodal biometric recognition using iris feature extraction and palmprint features

Abstract: A Biometric system is essentially a pattern recognition system that makes use of biometric traits to recognize individuals. Authentication systems built on only one biometric modality may not fulfill the requirements of demanding applications in terms of properties such as performance, acceptability and distinctiveness. Most of the unimodal biometrics systems have problems such as noise in collected data, intra-class variations, inter-class variations, non universality etc. Some of these limitations can be overcome by multiple source of information for establishing identity; such systems are known as multimodal biometric systems. In this paper a multi modal biometric system of iris and palm print based on Wavelet Packet Analysis is described. The most unique phenotypic feature visible in a person's face is the detailed texture of each eye's iris. Palm is the inner surface of a hand between the wrist and the fingers. Palmprint is referred to principal lines, wrinkles and ridges on the palm. The visible texture of a person's iris and palm print is encoded into a compact sequence of 2-D wavelet packet coefficients, which generate a “feature vector code”. In this paper, we propose a novel multi-resolution approach based on Wavelet Packet Transform (WPT) for texture analysis and recognition of iris and palmprint. The development of this approach is motivated by the observation that dominant frequencies of iris texture are located in the low and middle frequency channels. With an adaptive threshold, WPT sub images coefficients are quantized into 1, 0 or −1 as iris signature. This signature presents the local information of different irises. By using wavelet packets the size of the biometric signature of code attained is 960 bits. The signature of the new pattern is compared against the stored pattern after computing the signature of new input pattern. Identification is performed by computing the hamming distance.

Joint sparsity-based robust multimodal biometrics recognition

Abstract: Traditional biometric recognition systems rely on a single biometric signature for authentication. While the advantage of using multiple sources of information for establishing the identity has been widely recognized, computational models for multimodal biometrics recognition have only recently received attention. We propose a novel multimodal multivariate sparse representation method for multimodal biometrics recognition, which represents the test data by a sparse linear combination of training data, while constraining the observations from different modalities of the test subject to share their sparse representations. Thus, we simultaneously take into account correlations as well as coupling information between biometric modalities. Furthermore, the model is modified to make it robust to noise and occlusion. The resulting optimization problem is solved using an efficient alternative direction method. Experiments on a challenging public dataset show that our method compares favorably with competing fusion-based methods.

Towards real-time and rotation-invariant American Sign Language alphabet recognition using a range camera.

Abstract: The automatic interpretation of human gestures can be used for a natural interaction with computers while getting rid of mechanical devices such as keyboards and mice. In order to achieve this objective, the recognition of hand postures has been studied for many years. However, most of the literature in this area has considered 2D images which cannot provide a full description of the hand gestures. In addition, a rotation-invariant identification remains an unsolved problem, even with the use of 2D images. The objective of the current study was to design a rotation-invariant recognition process while using a 3D signature for classifying hand postures. A heuristic and voxel-based signature has been designed and implemented. The tracking of the hand motion is achieved with the Kalman filter. A unique training image per posture is used in the supervised classification. The designed recognition process, the tracking procedure and the segmentation algorithm have been successfully evaluated. This study has demonstrated the efficiency of the proposed rotation invariant 3D hand posture signature which leads to 93.88% recognition rate after testing 14,732 samples of 12 postures taken from the alphabet of the American Sign Language.

A survey for handwritten signature verification

Abstract: Signature verification is the process used to recognize an individual's handwritten signature. Signature verification can be divided into two main areas depending on the data acquisition method, off-line and on-line signature verification. In this paper we attempt to survey the signature verification based on three categories. First, judging from how to get the data signature which is off-line and on-line verification. Second, based on the technique used, that is rule-based approach, neural networks, hidden markov model and support vector machine. Third, based on preprocessing and feature extraction, which is thinning and line segmentation. Based on the survey, it was concluded that any method of verification has advantages and disadvantages. However, if viewed from the ease of implementation and performance, using neural networks or hidden markov models are the right choice. Depending on the data acquisition method, on-line verification is recommended to use than off-line verification.

Handwritten signature recognition in mobile scenarios: Performance evaluation

Abstract: Following the idea of improving our previous work on dynamic handwritten signature recognition on portable devices, a performance evaluation in a mobile scenario was done. A database with 11 users and 8 mobile devices (using stylus and finger) has been collected in order to study different parameters such as screen size, operative system and the interoperability between the devices. The evaluation was divided by 3 sessions of 20 signatures per device each; 20 skilled forgeries signatures per user were used also. The devices used were mobile phones, tablets, laptops and two specific devices for signing. The algorithm used to assess the signatures was a DTW-based signature recognition algorithm.

Off-line signature verification using G-SURF

Abstract: In the field of biometric authentication, automatic signature identification and verification has been a strong research area because of the social and legal acceptance and extensive use of the written signature as an easy method for authentication. Signature verification is a process in which the questioned signature is examined in detail in order to determine whether it belongs to the claimed person or not. 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. Sometimes, part-based signature verification can be useful when a questioned signature has lost its original shape due to inferior scanning quality. In order to address the above-mentioned adverse scenario, we propose a new feature encoding technique. This feature encoding is based on the amalgamation of Gabor filter-based features with SURF features (G-SURF). Features generated from a signature are applied to a Support Vector Machine (SVM) classifier. For experimentation, 1500 (50×30) forgeries and 1200 (50×24) genuine signatures from the GPDS signature database were used. A verification accuracy of 97.05% was obtained from the experiments.

Multimodal biometric person recognition system based on fingerprint & Finger-Knuckle-Print using correlation filter classifier

Abstract: Biometrics is an effective technology for personnel identity recognition, but uni-modal biometric systems which use a single trait for recognition will suffer from problems like noisy sensor data, non-universality, lack of distinctiveness of the biometric trait, and spoof attacks. These problems can be tackled by using multi-biometrics in the system. Hand-based person recognition provides a reliable, low-cost and user-friendly viable solution for a range of access control applications. As one of the most popular biometric traits, fingerprints (FP) are widely used in personal recognition. However, a novel hand-based biometric feature, Finger-Knuckle-Print (FKP), has attracted an increasing amount of attention. In this paper, FP and FKP are integrated in order to construct an efficient multi-biometric recognition system based on matching score level and image level fusion. In this study we use the minimum average correlation energy (MACE) and Unconstrained MACE (UMACE) filters in conjunction with two correlation plane performance measures, max peak value and peak-to-sidelobe ratio, to determine the effectiveness of this method. The experimental results showed that the designed system achieves an excellent recognition rate on the Hong Kong polytechnic university (PolyU) FKP and high resolution fingerprint database.

A system for hand gesture based signature recognition

Abstract: In this paper, we propose a user authentication system based on hand-gesture signature without the need of any handheld device. The system uses a depth image sensor to locate the fingertip and palm mass-center from detected hand region for trajectory processing. Apart from the positional information, the velocity and acceleration information are included as input features for trajectory matching. The system verification performance is evaluated in terms of equal error rate. In addition, an investigation of fusion at feature level is conducted for possible performance enhancement. Our empirical results show the potential of the proposed bare-hand in-air signature system.

Off-line English and Chinese signature identification using foreground and background features

Abstract: In the field of information security, the usage of biometrics is growing for user authentication. Automatic signature recognition and verification is one of the biometric techniques, which is only one of several used to verify the identity of individuals. In this paper, a foreground and background based technique is proposed for identification of scripts from bi-lingual (English/Roman and Chinese) off-line signatures. This system will identify whether a claimed signature belongs to the group of English signatures or Chinese signatures. The identification of signatures based on its script is a major contribution for multi-script signature verification. Two background information extraction techniques are used to produce the background components of the signature images. Gradient-based method was used to extract the features of the foreground as well as background components. Zernike Moment feature was also employed on signature samples. Support Vector Machine (SVM) is used as the classifier for signature identification in the proposed system. A database of 1120 (640 English+480 Chinese) signature samples were used for training and 560 (320 English+240 Chinese) signature samples were used for testing the proposed system. An encouraging identification accuracy of 97.70% was obtained using gradient feature from the experiment.

Multimodal biometric system using face and signature: a score level fusion approach

Abstract: A multimodal biometric system combines the different biometric traits and provides better recognition performance as compared to the systems based on single biometric trait or modality. In multimodal biometric system the fusion of the information can be done at various levels, but due to the ease in combining and accessing the scores generated by different matchers, the most common approach is the integration at the matching score level. Before combining, the scores should alter into a common domain, since different matchers generate heterogeneous scores. In this paper, we have studied performance of a single fast normalized cross-correlation matcher and simple sumrule fusion technique based on face and signature traits of a user. The experiments conducted on a database of 17 users indicate that simple sum of score fusion method results in better recognition performance than using single face or single signature based biometric system. However, experiments also reveal that the normalized cross-correlation based matcher gives better results, highlighting the need for a robust and efficient feature extraction technique. Keywordsbiometric systems, multi-biometric system, face, signature, score level fusion Advances in Computational Research ISSN: 0975-3273 & E-ISSN: 0975-9085, Volume 4, Issue 1, 2012 Introduction Biometrics refers to the physiological or behavioral characteristics of a person to authenticate his/her identity [1]. Due to the increasing demand of enhanced security systems biometric based person authentication system has led to an unprecedented interest of the researchers world-wide. Biometric systems based on single source of information are called unimodal systems. Although some unimodal systems (e.g. Face, Iris, Palm, Fingerprint) [2], (Figure 1 shows a typical fingerprint biometric system and popular biometric traits) have got considerable improvement in reliability and accuracy, they have suffered from enrollment problems due to non-universality of biometrics traits, susceptibility to biometric spoofing or insufficient accuracy caused by noisy data [3], Figure 2 shows the sample images of such affected traits. Hence, single biometric may not be able to achieve the desired performance requirement in real world applications. One of the methods to overcome these problems is to make use of multimodal biometric authentication systems, which combine information from multiple modalities to arrive at a decision. Studies have demonstrated that multimodal biometric systems can achieve better performance compared with unimodal systems. This paper presents score level fusion approach to multimodal biometrics using face and signature modalities. The paper is organized as follows. Approaches to multi-biometric system is discussed in Section 2 whereas different fusion levels and score level fusion techniques to multi-biometric system is illustrated in Section 3, Normalized cross correlation matching technique and simple sum based score level fusion are given in Section 4 and Section 5 respectively. Experimental results and conclusions to the work are presented in the last section of the paper. (a) Citation: Kazi M.M., et al (2012) Multimodal Biometric System Using Face and Signature: A Score Level Fusion Approach. Advances in Computational Research, ISSN: 0975-3273 & E-ISSN: 0975-9085, Volume 4, Issue 1, pp.-99-103. Copyright: Copyright©2012 Kazi M.M., et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Off-Line Bangla Signature Verification

Abstract: In the field of information security, biometric systems play an important role. Within biometrics, automatic signature identification and verification has been a strong research area because of the social and legal acceptance and extensive use of the written signature as an individual authentication. Signature verification is a process in which the questioned signature is examined in detail in order to determine whether it belongs to the claimed person or not. Despite substantial research in the field of signature verification involving Western signatures, very few works have been dedicated to non-Western signatures such as Chinese, Japanese, Arabic, or Persian etc. In this paper, the performance of an off-line signature verification system involving Bangla signatures, whose style is distinct from Western scripts, was investigated. The Gaussian Grid feature extraction technique was employed for feature extraction and Support Vector Machines (SVMs) were considered for classification. The Bangla signature database employed in the experiments consisted of 3000 forgeries and 2400 genuine signatures. An encouraging accuracy of 90.4% was obtained from the experiments.

Signature Verification Using Morphological Features Based on Artificial Neural Network

Abstract: For identification of a particular human being signatures prove to be an important biometric. 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 cbn based on parameters extracted from the signature using various image processing techniques. This paper presents a proposed method for verifying offline-signatures .Novel features are used for classification of signatures. A Feed Forward Neural Network will be used for verifying signatures and to determine its accuracy.

Some issues of biometrics: technology intelligence, progress and challenges

Abstract: Biometrics has been used in healthcare systems, banking and finance, energy systems, access control in computer centres, military, and homeland security such as e-passport, border crossing control, airport security, criminal identification, and fighting terrorists. Biometric technologies such as face, fingerprint and iris recognition, multi-biometrics, and their technology intelligence, applications, and advances were studied. 13 attributes of six biometric technologies including face, fingerprint, iris, voice, hand geometry and signature recognition, and false reject rates (FRR) and false accept rates (FAR) of the six biometric technologies were summarised and compared based on technology evaluation or scenario evaluation. Challenges and future research of biometrics were also discussed.

Biometric image protection based on discrete cosine transform watermarking technique

Abstract: This paper presents combined face recognition and watermarking technique to secure a biometric image and maintain the recognition rate. These days, with advanced technology, the biometric data can be stolen and faked which may be used in other applications that utilize the same biometric feature. The discrete cosine transform (DCT) watermarking technique is used to certify the biometric image belong to the legitimate user. We have tested the proposed scheme under several attacks. The results of the experimentations show that the face recognition rate performance almost does not degrade due to watermark embedding and the proposed scheme is robust against several attacks.

Score level fusion based multimodal biometric identification (Fingerprint & voice)

Abstract: Feature level based monomodal biometric systems perform person recognition based on a multiple sources of biometric information and are affected by problems like integration of evidence obtained from multiple cues and normalization of features codes since they are heterogeneous, in addition of monomodal biometric systems problems like noisy sensor data, non-universality and lack of individuality of the chosen biometric trait, absence of an invariant representation for the biometric trait and susceptibility to circumvention. Some of these problems can be alleviated by using multimodal biometric systems that consolidate evidence from scores of multiple biometric systems. In this work, we address two important issues related to score level fusion. We have studied the performance of a score level fusion based multimodal biometric system against different monomodal biometric system based on voice, fingerprint modalities and a bimodal biometric system based on feature level fusion of the same modalities. These systems have been evaluated in terms of their efficiency and identification rate on a close group from the test data. These results are shown using cumulative match characteristic curve.

Electronic signature based on digital signature and digital watermarking

Abstract: In this paper, an electronic signature algorithm, using a combined technology of digital signature, digital watermarking and time stamp, is presented. In this algorithm timestamp information is added to digital signature data, improving the signature safety and anti- offensive. A digital watermark embedding algorithm based on wavelets transform module maximum is designed. Using this algorithm the signature information can be embedded into the signature image as a watermark, enhancing the signature information hidden. The experiment shows that the algorithm can meet very well the requirements of contract signature in e-commerce transactions. This algorithm not only implements authentication, but also ensures the integrity and non-tampering of the contract.

A Survey of Gait Recognition Approaches Using PCA and ICA

Abstract: Human identification by gait has created a great deal of interest in computer vision community due to its advantage of inconspicuous recognition at a relatively far distance Biometric systems are becoming increasingly important, since they provide more reliable and efficient means of identity verification Biometric gait Analysis (ie recognizing people from the way they walk) is one of the recent attractive topics in biometric research It has been receiving wide attention in the area of Biometric In Gait biometric research there are various gait recognition approaches are available In this paper, the gait recognition approaches such as “Wavelet Descriptor with ICA”, and “Hough transform with PCA” are compared and discussed

A power wheelchair controlled using hand gestures, a single sEMG sensor, and guided under-determined source signal separation

Abstract: Surface Electromyographic signals (sEMG) find applications in many areas such as rehabilitation, prosthesis and human-machine interaction. Systems reliant on these muscle-generated electrical signals require some form of machine learning algorithm for recognition of specific patterns of muscle activity. Those systems vary in terms of the signal detection methods, the feature selection and the classification algorithm used, however, in all those cases, the use of multiple sensors is a constant requirement. In this paper, we present a power wheelchair control system that relies on a single sEMG sensor and a new technique for signature recognition called Guided Under-determined Source Signal Separation (GUSSS). Compared to other approaches in the literature, the proposed technique achieves comparable results even when using a simple distance classifier and a very small number of features.

Authentication based on signature verification using position, velocity, acceleration and Jerk signals

Abstract: Signature verification techniques utilize many different characteristics of an individual. The selection of signature features is critical in determining the performance of a signature verification system. Even though it is critical to select a suitable set of features to be extracted, emphasis has to be put into selecting an appropriate classifier for the features selected. This paper evaluates 19 dynamic features viewpoint classification error and discrimination capability between genuine and forgery signatures. A modified distance of DTW algorithm is proposed to improve performance of verification phase. The proposed system is evaluated on the public SVC2004 signature database. The experimental results show that first, the most discriminate and consistent features are velocity-based. Second, average EER for proposed algorithm in comparison with the general DTW algorithm show a relative decrease 46.4%.

Off-line Uyghur signature recognition based on modified grid information features

Abstract: Many techniques have been published on handwriting signature recognition, but none of these techniques presented are about Uyghur handwritten signature due to its complex nature. In this paper, we propose methods for off-line signature recognition for Uyghur handwriting first time. The signature images were pre-processed based on the nature of Uyghur signature. The preprocessing included noise reduction, binarization and normalization. Then multi-dimensional modified grid information features were extracted according to the character of Uyghur signature and its writing style. Finally, three kinds of classification techniques were used: Euclidean distance (ED) classifier, K nearest neighbor (K-NN) classifier and Bayes classifier. Experiments were performed using Uyghur signature samples from 50 different people with 1000 signatures. A promising result of 93.53% average correct recognition rate was achieved.

A Survey on Offline Signature Recognition and Verification Schemes

Abstract: Signature has been a distinguishing biometric feature through ages. They are extensively used as a means of personal verification; therefore an automatic verification system is needed. Even today thousands of financial and business transactions are being authorized via signatures. Signature verification finds its application in a large number of fields starting from online banking, passport verification systems to even authenticating candidates in public examinations from their signatures. This paper represents a brief survey on various off-line signature recognition & verification schemes.

Improved Biometric Recognition and Identification of Human Iris Patterns Using Neural Networks

Abstract: A biometric system provides automatic identification of an individual based on a unique feature or characteristic possessed by the individual. Iris recognition is regarded as the most reliable and accurate biometric identification system available. An approach for accurate Biometric Recognition and Identification of Human Iris Patterns using Neural Network has been illustrated by gopikrishnan et. al. It has been concluded by Yingzi Du et. al that the partial iris portion of the iris pattern describes the uniqueness and the pupil has no direct effect on the accuracy of the biometric recognition. In this paper the Iris recognition has been carried out employing a template of size 10 x 480 pixels instead of 20 x 480 pixels as employed in the earlier paper. The results of the two sizes of the templates have been compared and it has been observed that the accuracy of the results obtained with the limited template size is comparable with that of the one with the full size. The reason for this is also discussed ...

Biometric template protection using turbo codes and modulation constellations

Abstract: In this paper we propose a general biometric cryptosystem framework inspired by the code-offset sketch Specifically, the properties of digital modulation and turbo codes with soft-decoding are exploited to design a template protection system able to guarantee high performance in terms of both verification rates and security, also when dealing with biometrics characterized by a high intra-class variability The effectiveness of the presented approach is evaluated by its application as case study to on-line signature recognition