scispace - formally typeset
Search or ask a question

Showing papers on "Signature recognition published in 2017"


Journal ArticleDOI
TL;DR: This paper proposes a development of the synthetic generation of static handwritten signatures based on motor equivalence theory in which dynamic information is generated and a unified comprehensive synthesizer for both static and dynamic signature synthesis is provided.
Abstract: The synthetic generation of static handwritten signatures based on motor equivalence theory has been recently proposed for biometric applications. Motor equivalence divides the human handwriting action into an effector dependent cognitive level and an effector independent motor level. The first level has been suggested by others as an engram, generated through a spatial grid, and the second has been emulated with kinematic filters. Our paper proposes a development of this methodology in which we generate dynamic information and provide a unified comprehensive synthesizer for both static and dynamic signature synthesis. The dynamics are calculated by lognormal sampling of the 8-connected continuous signature trajectory, which includes, as a novelty, the pen-ups. The forgery generation imitates a signature by extracting the most perceptually relevant points of the given genuine signature and interpolating them. The capacity to synthesize both static and dynamic signatures using a unique model is evaluated according to its ability to adapt to the static and dynamic signature inter- and intra-personal variability. Our highly promising results suggest the possibility of using the synthesizer in different areas beyond the generation of unlimited databases for biometric training.

72 citations


Journal ArticleDOI
TL;DR: An efficient offline signature verification method based on an interval symbolic representation and a fuzzy similarity measure and it is noted that the proposed method always outperforms when the number of training samples is eight or more.
Abstract: In this paper, an efficient offline signature verification method based on an interval symbolic representation and a fuzzy similarity measure is proposed. In the feature extraction step, a set of local binary pattern-based features is computed from both the signature image and its under-sampled bitmap. Interval-valued symbolic data is then created for each feature in every signature class. As a result, a signature model composed of a set of interval values (corresponding to the number of features) is obtained for each individual’s handwritten signature class. A novel fuzzy similarity measure is further proposed to compute the similarity between a test sample signature and the corresponding interval-valued symbolic model for the verification of the test sample. To evaluate the proposed verification approach, a benchmark offline English signature data set (GPDS-300) and a large data set (BHSig260) composed of Bangla and Hindi offline signatures were used. A comparison of our results with some recent signature verification methods available in the literature was provided in terms of average error rate and we noted that the proposed method always outperforms when the number of training samples is eight or more.

61 citations


Proceedings ArticleDOI
01 Oct 2017
TL;DR: The LPSNet is proposed, an end-to-end deep neural network based hand gesture recognition framework with novel log path signature features and a new method based on PS and LPS to effectively combine RGB and depth videos.
Abstract: Hand gesture recognition is gaining more attentions because it's a natural and intuitive mode of human computer interaction. Hand gesture recognition still faces great challenges for the real-world applications due to the gesture variance and individual difference. In this paper, we propose the LPSNet, an end-to-end deep neural network based hand gesture recognition framework with novel log path signature features. We pioneer a robust feature, path signature (PS) and its compressed version, log path signature (LPS) to extract effective feature of hand gestures. Also, we present a new method based on PS and LPS to effectively combine RGB and depth videos. Further, we propose a statistical method, DropFrame, to enlarge the data set and increase its diversity. By testing on a well-known public dataset, Sheffield Kinect Gesture (SKIG), our method achieves classification rate as 96.7% (only use RGB videos) and 98.7% (combining RGB and Depth videos), which is the best result comparing with state-of-the-art methods.

41 citations


Journal ArticleDOI
31 May 2017-Entropy
TL;DR: A signature recognition approach based on a probabilistic neural network (PNN) and wavelet transform average framing entropy (AFE) is proposed and achieved the best recognition rate result whereby the threshold entropy reached 92%.
Abstract: Handwritten signatures are widely utilized as a form of personal recognition However, they have the unfortunate shortcoming of being easily abused by those who would fake the identification or intent of an individual which might be very harmful Therefore, the need for an automatic signature recognition system is crucial In this paper, a signature recognition approach based on a probabilistic neural network (PNN) and wavelet transform average framing entropy (AFE) is proposed The system was tested with a wavelet packet (WP) entropy denoted as a WP entropy neural network system (WPENN) and with a discrete wavelet transform (DWT) entropy denoted as a DWT entropy neural network system (DWENN) Our investigation was conducted over several wavelet families and different entropy types Identification tasks, as well as verification tasks, were investigated for a comprehensive signature system study Several other methods used in the literature were considered for comparison Two databases were used for algorithm testing The best recognition rate result was achieved by WPENN whereby the threshold entropy reached 92%

27 citations


Journal ArticleDOI
TL;DR: A novel video-based system for in-air signature verification is proposed, using the fingertip tracking for generating unique signature trajectory from the in- air signing videos and a Gaussian distribution-based fusion algorithm for solving the signature length problem.

22 citations


Proceedings ArticleDOI
01 Aug 2017
TL;DR: Comparisons and evaluations of recognition accuracy and running speed show that PCA + SVM achieves the best recognition result, which is over 95% for certain training data and eigenface sizes.
Abstract: Facial recognition is a challenging problem in image processing and machine learning areas. Since widespread applications of facial recognition make it a valuable research topic, this work tries to develop some new facial recognition systems that have both high recognition accuracy and fast running speed. Efforts are made to design facial recognition systems by combining different algorithms. Comparisons and evaluations of recognition accuracy and running speed show that PCA + SVM achieves the best recognition result, which is over 95% for certain training data and eigenface sizes. Also, PCA + KNN achieves the balance between recognition accuracy and running speed.

17 citations


Journal ArticleDOI
TL;DR: A novel approach is proposed here for depth video based human activity recognition, using joint-based spatiotemporal features of depth body shapes and hidden Markov models that shows superior recognition performance compared to other conventional activity recognition approaches.
Abstract: In recent years, human activity recognition from video has been getting considerable research attentions by computer vision researchers due to its prominent applications in various fields such as surveillance environments, human computer interactions, and smart home healthcare. For instance, activity recognition can be used in a surveillance environment to alert the related authority of potential dangerous behaviors. Similarly, the activity recognition can improve the human computer interaction (HCI) in an entertainment environment such as the automatic recognition of different player's actions in a game so as to create an avatar to play on behalf for the player. Furthermore, the activity recognition can help the rehabilitation of patients in a healthcare system where patient's action recognition can help to facilitate the rehabilitation processes. Basically, a video-based activity recognition system consists of many prominent goals, one of which is to provide information based on people's behavior in order to allow the system to proactively assist them with their tasks. A novel approach is proposed here for depth video based human activity recognition, using joint-based spatiotemporal features of depth body shapes and hidden Markov models. From depth video, different body parts of human activities are first segmented using a trained random forest. Spatial features consisting of the 3-D body joint pair angles, the mean of the depth values, the variance of the depth values, and the area of each segmented body part are combined with the motion features representing the magnitude and direction of each joint in the next frame to build the spatiotemporal features in a frame. The activity features are then further enhanced using generalized discriminant analysis to classify them nonlinearly in order to convert them to more robust features. Finally, the features are utilized for training distinguished activity hidden Markov models that can be later used for recognition. The proposed approach shows superior recognition performance compared to other conventional activity recognition approaches.

16 citations


Journal ArticleDOI
TL;DR: An offline signature recognition and verification system which employed an efficient fuzzy Kohonen clustering networks (EFKCN) algorithm and a good signature recognition result can be developed to assist the verification system as well as the personal data verification system.

16 citations


Proceedings ArticleDOI
01 May 2017
TL;DR: The results show that there is a subset of features extracted from the ECG signal that provides high recognition rates, and this papers analyses the impact of some feature selection strategies like Genetic Algorithm, Memetic Algorithm and Particle Swarm Optimization on the performance of Biometric Systems based on ECG using K-Nearest Neighbours, Support Vector Machines, Optimum Path Forest and a Euclidean Distance Classifier for classification task.
Abstract: Currently the demand for the development of more precise and reliable methods of person identification have received attention from the academic community and industry, with Biometrics being one of these new approaches. The term ‘Biometrics’ is used to refer to identification techniques based on physical or behavioural characteristics. As biometric recognition becomes increasingly popular, the fear of circumvention, obfuscation and replay attacks is a rising concern. Since the traditional biometric modalities (face, iris and fingerprint) are not able to supply the needs of every possible security requirement, numerous emerging biometric modalities are presented, trying to fill the gap. Biomedical signals, like electrocardiogram (ECG) and electroencephalogram (EEG), have been proposed as emerging biometric modalities. The advantages of using the ECG for biometric recognition can be summarized as universality, permanence, uniqueness, robustness to attacks, liveness detection. According to the utilized features, the existing ECG based biometric systems can be classified to fiducial, non-fiducial and hybrids systems. This papers analyses the impact of some feature selection strategies like Genetic Algorithm, Memetic Algorithm and Particle Swarm Optimization on the performance of Biometric Systems based on ECG using K-Nearest Neighbours, Support Vector Machines, Optimum Path Forest and a Euclidean Distance Classifier for classification task. The results show that there is a subset of features extracted from the ECG signal that provides high recognition rates.

15 citations


Proceedings ArticleDOI
01 Feb 2017
TL;DR: This paper proposed and implemented an innovative approach based on upper and lower envelope and Eigen values techniques that gives better performance than already established offline signature recognition methods.
Abstract: Automatic signature recognition is most active area of research with number of applications such as financial, official work, bank cheque, business etc. To obtain maximum possible security from fake signature there is emergent need for a signature recognition, which can assure good results and gives better performance than already established offline signature recognition methods. In this paper, we proposed and implemented an innovative approach based on upper and lower envelope and Eigen values techniques. Envelope represents the shape of the signature. The feature set consists of features such as large and small Eigen values computed from upper envelope and lower envelope and its union values. Both the envelopes are fused by performing union operation and their covariance is computed. The difference and ratios of high and low points of both the envelopes are computed. Lastly average values of both the envelopes are obtained. These features set are coupled with support vector machine classifier that lead to 98.5 % of accuracy.

14 citations


Proceedings ArticleDOI
01 Aug 2017
TL;DR: A SIFT and a SURF algorithm which is used for enhanced offline signature recognition is proposed and it is found out that the use of SIFT with SVM-RBF kernel system, it has an accuracy of 98.75% compared that of SURF with S VM- RBF kernel it has a accuracy of 96.25%.
Abstract: The Signature recognition is known as the process to verify a writer by examining the signature upon samples has been studied and stored in the database. This process has two types: The offline and the online. This paper deals with the offline technique. This paper proposed a SIFT and a SURF algorithm which is used for enhanced offline signature recognition. This process, Bag-of-word features, was operated by making vector quantization technique, which outlined the key points for each training image inside a unified dimensional histogram. We put features of bag-of-word inside multiclass Support Vector Machine (SVM) classifier established upon the radial basis function (RBF) for a training and testing. We used Open CV C++ as an image processing tool and tool for feature extraction. In this paper, we compare the performance of SIFT on SVM based RBF kernel with SURF on SVM based RBF kernel. It was found out that the use of SIFT with SVM-RBF kernel system, it has an accuracy of 98.75% compared that of SURF with SVM-RBF kernel it has an accuracy of 96.25%.

Journal ArticleDOI
TL;DR: Using the results of a previous work, the vulnerabilities are detected and two presentation attack detection techniques have been implemented and a new evaluation has been performed, showing an improvement in the performance of written signature recognition.
Abstract: Handwritten signature recognition is a biometric mode that has started to be deployed. Therefore, it is necessary to analyze the robustness of the recognition process against presentation attacks, to find its vulnerabilities. Using the results of a previous work, the vulnerabilities are detected and two presentation attack detection techniques have been implemented. With such implementations, a new evaluation has been performed, showing an improvement in the performance. Error rates have been lowered from about 20% to below 3% under operational conditions.

Proceedings ArticleDOI
14 May 2017
TL;DR: A novel HMM-based gesture recognition scheme that can be implemented for developing an improved HCI system capable of providing enhanced performance and explores the high potential of Microsoft's Kinect sensor in gesture recognition by utilizing it in the data acquisition phase.
Abstract: Currently, gesture recognition from continuous video sequences is one of the most exciting research areas. This paper proposes a novel HMM-based gesture recognition scheme that can be implemented for developing an improved HCI system capable of providing enhanced performance. This framework explores the high potential of Microsoft's Kinect sensor in gesture recognition by utilizing it in the data acquisition phase. The primary novelty of the work lies in the choice of an active difference signature-based feature descriptor that contains time-warped information in a single sequence over the classically used geometric features. The discussed framework has been tested for 12 distinct gestures embodied by 60 different subjects and it is important to note that for all the gestures the proposed scheme has attained a fairly high recognition rate of nearly 90% which proves the worth of the present work in real time applications. Further, to check the efficacy of the newly formulated framework the performance of the same has been validated against the existing standard technologies.

Proceedings ArticleDOI
01 Feb 2017
TL;DR: A method for handwritten text recognition (HWR) of this font is proposed and a method for preprocessing and normalization of data and optical character recognition based on SVM classifier is proposed.
Abstract: Comenia script is a novel handwritten text introduced at primary schools in the Czech Republic This paper describes a method for handwritten text recognition (HWR) of this font In particular it proposes a method for preprocessing and normalization of data and optical character recognition based on SVM classifier We have trained and statistically evaluated several models, where we have focused on recognition of different styles of writing of the same characters — for the forensic purposes and identification of the author of a document The best model has achieved 9286 % accuracy without any further postprocessing, eg a spellchecker We also proposed using more than one classification model for character recognition that has shown to increase accuracy when compared to a single model approach

Proceedings ArticleDOI
01 Dec 2017
TL;DR: An offline signature recognition system which uses histogram of oriented gradients is presented and Feedforward backpropagation neural network is used for classification.
Abstract: The most common way of authenticating a document or financial transactions, especially cheques, is handwritten signatures. In most of the cases, verification of these signatures is done by visual inspection. A person compares the two signatures and accepts the given signature if it sufficiently matches with stored signature. In banks where thousands of cheques and scanned documents are to be processed every day, process of visually verifying the signatures become cumbersome and time consuming. Automating the signature verification will improve the situation and eliminate the possibility of forging. In this paper, an offline signature recognition system which uses histogram of oriented gradients is presented. Feedforward backpropagation neural network is used for classification. The system gives recognition rate of96.87% with 4 training sample per individual.

Journal ArticleDOI
TL;DR: An effective method for static signature recognition from spontaneous handwritten text images that relies on the presence of redundant patterns in the writing and its features and achieves promising results on publicly available data set.
Abstract: In this paper we propose an effective method for static signature recognition from spontaneous handwritten text images. Our method relies on different aspects of writing: the presence of redundant patterns in the writing and its features. Signatures are analyzed at small fragments in which we seek to extract the patterns that an individual employs frequently as he writes. We exploit different features of writing like orientation, centroid and contour by computing a set of features from writing samples at different levels of observations. Orientation like intersecting point, edge point and gradient change of signature achieve great success in feature description. These features are extracted from the standard signature database and extracted features are trained and tested by machine learning (ML) approach. The machine learning approaches like Bagging, Random subspace (RS) and REP tree are used for classification purpose. Bagging with 10 iterations and base learner achieved efficiency upto 88 %. RS randomly selects features from feature set and creates new feature set. It uses decision tree as base classifier with different tree size. RS achieved efficiency same as Bagging but has more statistical errors. However, in case of REP tree we have achieved efficiency upto 75 %. The experimental results show that the Bagging and RS achieves promising results on publicly available data set.


Journal ArticleDOI
TL;DR: The experimental results demonstrate that the system based on two dimensional hidden Markov models is able to achieve a good recognition rate for 2D, 3D and multimodal (2D+3D) face images recognition, and is faster than ICP method.
Abstract: *e-mail: januszb@icis.pcz.pl Abstract. The paper presents a new solution for the face recognition based on two-dimensional hidden Markov models. The traditional HMM uses one-dimensional data vectors, which is a drawback in the case of 2D and 3D image processing, because part of the information is lost during the conversion to one-dimensional features vector. The paper presents a concept of the full ergodic 2DHMM, which can be used in 2D and 3D face recognition. The experimental results demonstrate that the system based on two dimensional hidden Markov models is able to achieve a good recognition rate for 2D, 3D and multimodal (2D+3D) face images recognition, and is faster than ICP method.

Proceedings ArticleDOI
01 Sep 2017
TL;DR: A framework for a smartphone based gait recognition system with application of SL for biometric data fusion and a method for combining such scores from multiple comparators using Subjective Logic (SL), as it takes uncertainty into account while performing to biometric fusion.
Abstract: The performance of a biometric system gets affected by various types of errors such as systematic errors, random errors, etc. These kinds of errors usually occur due to the natural variations in the biometric traits of subjects, different testing, and comparison methodologies. Neither of these errors can be easily quantifiable by mathematical formulas. This behavior introduces an uncertainty in the biometric verification or identification scores. The combination of comparison scores from different comparators or combination of multiple biometric modalities could be a better approach for improving the overall recognition performance of a biometric system. In this paper, we propose a method for combining such scores from multiple comparators using Subjective Logic (SL), as it takes uncertainty into account while performing to biometric fusion. This paper proposes a framework for a smartphone based gait recognition system with application of SL for biometric data fusion.

Journal ArticleDOI
TL;DR: This paper presents a suitable and viable combination of a face recognition system and a watermarking system, namely a PCA—DCT combination, as a new watermarked face recognition scheme that will ensure the authenticity of the data being transmitted in the face Recognition system, which will then increase its level of security.
Abstract: This paper presents a proposal for a suitable and viable combination of a face recognition system and a watermarking system, namely a PCA--DCT combination, as a new watermarked face recognition scheme that will ensure the authenticity of the data being transmitted in the face recognition system, which will then increase its level of security. The emphasis is on recognizing and rejecting stolen biometric data reintroduced into the system. The research begins with an analysis of biometric systems, with an emphasis on face recognition systems, and in particular with reference to the recorded threats on such systems, Biometric watermarking algorithms proposed by previous researchers within the face recognition environment are then studied, noting their proposed solutions to the said threats. This would then give a good idea towards a watermarked face recognition scheme to be proposed to enhance the security of face recognition systems, especially in terms of the authenticity of the data being transmitted. This watermarked face recognition scheme is the main objective, which will be then worked into the PCA--DCT combination, followed by a check on all the 8 possible locations where data may be intercepted and/or reintroduced. All the results produced are positive, apart from a few situations that will have to be left for future work. Non degradation of the individual PCA and DCT systems due to the combination is also checked and experimented on, again with positive results. Finally, the robustness of the watermarked face recognition scheme is experimented on to evaluate its resilience against attacks.

Journal ArticleDOI
TL;DR: There is a need to develop a better combined approach which may provide better recognition rate as compared to individual methods, which will help the researchers to know the basic difference between the explained feature extraction techniques.
Abstract: Objectives: To provide detailed study of all the algorithms and analyse them in a way to conclude that an integration approach describes the best combination for voice recognition in terms of recognition rate. Methods: Voice processing is a process in which words of a speaker are recognized by the information of the waves. There are number of algorithms used for voice recognition named as Perceptual Linear Prediction, Linear Predictive Code, Mel Frequency Cepstral Coefficient, Dynamic Time Warping etc. Findings: Graph is used to depict the recognition rate of all the voice recognition techniques with different types of classifiers named as HMM (Hidden Markov Model), DTW ( Dynamic Time Wrapping), VQ (Vector Quantization), SVM (Support Vector Machine) etc. which clearly describes that the hybrid approach may provide better results as compared to individual methods. Performance and recognition rate is not so good by using individual techniques because it does not provide better recognition rate while taking into consideration the security of an individual living alone at home. After the comparative analysis, it is concluded that there is a need to develop a better combined approach which may provide better recognition rate as compared to individual methods. This paper will help the researchers to know the basic difference between the explained feature extraction techniques. Application: Main application we are using is Voice Recognition in order to provide security to an individual living alone at home.

Journal ArticleDOI
25 May 2017
TL;DR: A new physiological trait based on the human body’s electrical response to a square pulse signal, called pulse-response, is proposed and how this biometric characteristic can be used to enhance security in the context of two example applications: an additional authentication mechanism in PIN entry systems and a means of continuous authentication on a secure terminal.
Abstract: Biometric characteristics are often used as a supplementary component in user authentication and identification schemes. Many biometric traits, both physiological and behavioral, offering a wider range of security and stability, have been explored. We propose a new physiological trait based on the human body’s electrical response to a square pulse signal, called pulse-response, and analyze how this biometric characteristic can be used to enhance security in the context of two example applications: (1) an additional authentication mechanism in PIN entry systems and (2) a means of continuous authentication on a secure terminal. The pulse-response biometric recognition is effective because each human body exhibits a unique response to a signal pulse applied at the palm of one hand and measured at the palm of the other. This identification mechanism integrates well with other established methods and could offer an additional layer of security, either on a continuous basis or at log-in time. We build a proof-of-concept prototype and perform experiments to assess the feasibility of pulse-response for biometric authentication. The results are very encouraging, achieving an equal error rate of 2% over a static dataset and 9% over a dataset with samples taken over several weeks. We also quantize resistance to attack by estimating individual worst-case probabilities for zero-effort impersonation in different experiments.

Proceedings ArticleDOI
01 Nov 2017
TL;DR: The results show that compara-tive attributes outperform absolute attributes for semi-automatic signature recognition with Equal Error Rates ranging from 5.5% for random comparisons to 21.2% for simulated forgeries.
Abstract: This work analyzes the performance of comparative attributes labeled by humans for handwritten semi-automatic signature recognition Despite the large deployment of automatic sys-tems, humans have still an important role in many tasks relat-ed to handwritten signature recording and verification How humans can help to improve these processes is a primary aim of different research lines Comparative attributes try to ex-ploit the abilities of humans to extract discriminant infor-mation of the signatures Instead of absolute attributes (eg is this stroke vertical?), the comparative attributes offer richer responses (eg how vertical is this stroke?) In this work we present a new semi-automatic signature labeling interface in-spired by Forensic Document Examiners (FDE) Fifteen com-parative attributes have been labeled by 21 laymen, where each one carries out the labeling of 28 signatures from 130 users of the publicly available corpus BiosecurID database Through the manual labeling, a new Bio-HSL (Biometric-Handwritten Signatures Labelling) database is generated, which contains 4,968,600 signature attributes The results show that compara-tive attributes outperform absolute attributes for semi-automatic signature recognition with Equal Error Rates ranging from 55% for random comparisons to 212% for simulated forgeries

Proceedings ArticleDOI
26 Jun 2017
TL;DR: This paper presents a text-independent speaker recognition method particularly suitable for identification in AmI environments that reduced the size of the template with respect to traditional approaches based on Gaussian Mixture Models and achieved better identification accuracy.
Abstract: Biometric systems are enabling technologies for a wide set of applications in Ambient Intelligence (AmI) environments. In this context, speaker recognition techniques are of paramount importance due to their high user acceptance and low required cooperation. Typical applications of biometric recognition in AmI environments are identification techniques designed to recognize individuals in small datasets. Biometric recognition methods are frequently deployed on embedded hardware and therefore need to be optimized in terms of computational time as well as used memory. This paper presents a text-independent speaker recognition method particularly suitable for identification in AmI environments. The proposed method first computes the Mel Frequency Cepstral Coefficients (MFCC) and then creates Information Set Features (ISF) by applying a fuzzy logic approach. Finally, it estimates the user's identity by using a hierarchical classification technique based on computational intelligence. We evaluated the performance of the speaker recognition method using signals belonging to the NIST-2003 switchboard speaker database. The achieved results showed that the proposed method reduced the size of the template with respect to traditional approaches based on Gaussian Mixture Models (GMM) and achieved better identification accuracy.

Journal ArticleDOI
TL;DR: A novel method using Pseudo-Inked Signature for online signature recognition is proposed, which combines three types of information of pen pressure value, pen tilting angle, and pen theta angle by mimicking the inked effect of real pen writing.
Abstract: As human–robot interaction is widely and increasingly used, automated user verification has become a necessary condition for system access. Signature recognition is one of the representative methods for user verification. In this paper, a novel method using Pseudo-Inked Signature for online signature recognition is proposed. Pseudo-Inked Signature consists of three types of information of pen pressure value, pen tilting angle, and pen theta angle during online signature writing. We propose a fusion method for three different types of information by mimicking the inked effect of real pen writing. Besides a style of penmanship, Pseudo-Inked Signature reflects the characteristics of handwriting behavior. Therefore, it can make different Pseudo-Inked Signature even though the original signature images from different users look very similar to each other. Similarly, it can also make more similar Pseudo-Inked Signatures even though the original signature images from the same user look somewhat different to each other. In addition, since only one gray-scale image is dealt with to represent the signature style of a person by Pseudo-Inked Signature image, it is efficient and very easy to handle. Finally, we tested user verification experiments using k-NN classifier. The experimental results show that Pseudo-Inked Signature is good enough for the real application.

Proceedings ArticleDOI
15 May 2017
TL;DR: In this non-real-time signature recognition application, it has been tried to reduce the process load and memory requirement by using deep learning method by using convolutional neural network.
Abstract: Nowadays, with the increase of biometric studies, the diversity of biometric data increases and new methods are used in evaluation methods. Traditional biometrics, such as face, fingerprints, handpieces, now leave their place to a variety of biometrics, which contain characteristic information about more people and include movement information. In this study, the performance of the deep learning method based on convolutional neural network (CNN) is demonstrated on a nonlinear signature recognition problem. In this non-real-time signature recognition application, it has been tried to reduce the process load and memory requirement by using deep learning method. Two data sets with different participant numbers were created in the study. The performance and reliability of the system are examined by various ratios of training and testing data on these data sets.

Proceedings ArticleDOI
01 Mar 2017
TL;DR: Signature verification is the process used to verify an individual's hand written signature is genuine or forged signature.
Abstract: Very large percentage of daily financial transactions is generally carried out on the basis of verification of signatures. Therefore signature plays an important role both for authentication and authorization of any legal documents. Signature verification is the process used to verify an individual's hand written signature is genuine or forged signature. Classification of recognition rate of genuine signature and rejection rate of forgery signature is 95% and 5% respectively.

Journal ArticleDOI
TL;DR: ABiometric images recognition system able to recognize biometric images-eye and DNA marker and Cambridge optical correlator is used as an image comparator based on similarity of images in the recognition phase.
Abstract: The aim of this paper is to design a biometric images recognition system able to recognize biometric images-eye and DNA marker. The input scenes are processed by user-friendly software created in C# programming language and then are compared with reference images stored in database. In this system, Cambridge optical correlator is used as an image comparator based on similarity of images in the recognition phase.

Book ChapterDOI
28 Oct 2017
TL;DR: The experimental results indicated that modified corner curve features in this paper can efficiently capture the writing style of Uyghur signature.
Abstract: In this paper, a local central line features based off-line signature recognition method proposed for Uyghur handwritten signature. The signature images were pre-processed based on the nature of Uyghur signature firstly. Then global central line features (GCLF-16, GCLF-24, and GCLF-32), local central line features from two horizontally centers (2LCLF-16H, 2LCLF-24H, and 2LCLF-32H) and local central line features from two vertically centers (2LCLF-16V, 2LCLF-24V, and 2LCLF-32V) were extracted respectively. Experiments were performed using Euclidean distance based similarity measuring method and non-linear SVM classifier for Uyghur signature samples from 75 different people with 1500 signatures, two kinds of experiments were performed for and variations in the number of training and testing datasets, and a high recognition rate of 96.8% was achieved with 2LCLF-32H. The experimental results indicated that modified corner curve features in this paper can efficiently capture the writing style of Uyghur signature.

Journal ArticleDOI
TL;DR: This is the first investigation towards the use of score normalization to enhance adaptive biometric systems dealing with the change of user features over time and the experimental results show that the performance gain brought by adaptation can have a higher overall impact than scorenormalization alone.