Signature recognition

About: Signature recognition is a research topic. Over the lifetime, 2138 publications have been published within this topic receiving 37605 citations.

A comparative study of signature recognition problem using statistical features and artificial neural networks

Abstract: This paper summarizes a research effort for an off-line signature recognition system. Neural network is used to address this problem because the learning and generalization abilities of NNs enable them to cope up with the diversity and the variation of human signatures. Since neural network have proven performance in other pattern recognition tasks such as character recognition therefore, it is equally suitable for the task of signature recognition. In this paper we present a comparative study of signature recognition comprises of three different neural networks i.e., feed-forward-back propagation neural network, competitive and probabilistic neural network. We have proved by experiment and analysis that probabilistic neural network is best suited to deal with signature recognition problem with an average of 100% accuracy.

Application of Bayesian networks for pattern recognition: Character recognition case

Abstract: Pattern recognition is a wide field in progress In particular, handwriting recognition has known a great development in the recent years Several solutions have been directed towards the use of Bayesian networks, which have shown their ability to solve complex problems in many areas, and that is thanks to their ability to model inaccuracies, which are lacunae highly present in the manuscript field In this paper, we recall the basics of these networks and the difficulties come across in their learning and inference algorithms to make a good decision We present a state of using the BNs and especially RBDs in the pattern recognition and more exactly in the character recognition We show, through the various considered works, the contribution of this technique in solving the limitations of the Markov models and its ability to represent efficiently the temporal notion and the dependencies between the variables during the writing process Moreover, we retain the recorded limitations and some development perspectives

Neural Network Based Accurate Biometric Recognition and Identification of Human Iris Patterns

Abstract: Problem statement: 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. Approach: Most commercial iris recognition systems use patented algorithms developed by Daugman and these algorithms are able to produce perfect recognition rates. However, published results have usually been produced under favorable conditions and there have been no independent trials of the technology. Results: In this study after providing brief picture on development of various techniques for iris recognition, hamming distance coupled with neural network based iris recognition techniques were discussed. Perfect recognition on a set of 150 eye images has been achieved through this approach. Further, Tests on another set of 801 images resulted in false accept and false reject rates of 0.0005 and 0.187% respectively, providing the reliability and accuracy of the biometric technology. Conclusion/Recommendations: This study provided results of iris recognition performed applying Hamming distance, Feed forward back propagation, Cascade forward back propagation, Elman forward back propagation and perceptron. It has been established that the method suggested applying perceptron provides the best accuracy in respect of iris recognition with no major additional computational complexity.

Pulse-Response: Exploring Human Body Impedance for Biometric Recognition

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.

The use of EEG signals for biometric person recognition

Abstract: This work is devoted to investigating EEG-based biometric recognition systems. One potential advantage of using EEG signals for person recognition is the difficulty in generating artificial signals with biometric characteristics, thus making the spoofing of EEG-based biometric systems a challenging task. However, more works needs to be done to overcome certain drawbacks that currently prevent the adoption of EEG biometrics in real-life scenarios: 1) usually large number of employed sensors, 2) still relatively low recognition rates (compared with some other biometric modalities), 3) the template ageing effect. The existing shortcomings of EEG biometrics and their possible solutions are addressed from three main perspectives in the thesis: pre-processing, feature extraction and pattern classification. In pre-processing, task (stimuli) sensitivity and noise removal are investigated and discussed in separated chapters. For feature extraction, four novel features are proposed; for pattern classification, a new quality filtering method, and a novel instance-based learning algorithm are described in respective chapters. A self-collected database (Mobile Sensor Database) is employed to investigate some important biometric specified effects (e.g. the template ageing effect; using low-cost sensor for recognition). In the research for pre-processing, a training data accumulation scheme is developed, which improves the recognition performance by combining the data of different mental tasks for training; a new wavelet-based de-noising method is developed, its effectiveness in person identification is found to be considerable. Two novel features based on Empirical Mode Decomposition and Hilbert Transform are developed, which provided the best biometric performance amongst all the newly proposed features and other state-of-the-art features reported in the thesis; the other two newly developed wavelet-based features, while having slightly lower recognition accuracies, were computationally more efficient. The quality filtering algorithm is designed to employ the most informative EEG signal segments: experimental results indicate using a small subset of the available data for feature training could receive reasonable improvement in identification rate. The proposed instance-based template reconstruction learning algorithm has shown significant effectiveness when tested using both the publicly available and self-collected databases.