Signature recognition

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

FPGA Implementation of Programmable Pulse Mode Neural Network with on Chip Learning for signature application

Abstract: This paper presents an implementation of a signature recognition system based on pulse mode multilayer neural networks with on chip learning. Taking advantage of the compactness of the multiplierless solutions of pulse mode operations, we apply an architecture, in which the synapse is made up with a DDFS and the neuron uses a nonlinear adder. A programmable activation function is proposed by means of an adjustable pulse multiplier so that the activation function slope can be adjusted without any added hardware cost. Good learning capability is obtained. As illustration, we consider a signature learning application. The corresponding design was implemented into an FPGA platform ( virtex II PRO XC2VP7).

Static signature verification for Farsi and Arabic signatures using dynamic time warping

Abstract: Signature verification is one of the central issues in our everyday life, especially in financial administration. Signature verification (SV) systems have been founded on human biometric features which are different from person to person. A desperate trouble with signatures is the mismatch between original signatures generated by an individual. In this paper, the existing methods dealing with these problems have been addressed. Then a tried-and-tested matching method based on Discrete Time Warping (DTW) is presented. Experiments are conducted to compare the suggested method with the most significant ones. The results demonstrated the potency of the introduced strategy.

The Challenge of Forgeries and Perception of Dynamic Signature Verification

Abstract: Dynamic Signature Verification (DSV) is unique among other biometric authentication technologies as there is no clearly defined method of creating a forgery. This research examined the perception of the signature to the forger (how easy an individual perceives the signature to be forged), and whether there were any characteristics common among the groupings of difficulty. The dynamic variables of the signature were then examined to establish which statistical variables were susceptible to forgery using forensic tools. Overall, it seems that both the genuine and impostor groups do not single out a specific dynamic trait within their judgment of an "easy" or "difficult" signature. Furthermore, it also shows that individuals have difficulty in assigning a speed to their signature - i.e. the perception of speed is different for each individual (both genuine and impostor), and additionally, both the impostors and genuine users ranked their signatures differently when asked about the perceived level of difficulty.

Theoretical Analysis and Experimental Study on Multimodal Biometric

Abstract: Multimodal biometric could overcome the drawbacks of single biometric by combining two or more biometric traits for personal identity verification. The theory and experiments of multimodal biometric were studied based on hand vein, iris and fingerprint. Simple Average and Weighting Average fusion algorithm, the classical information fusion methods, were analyzed and the constraint conditions for improving the recognition accuracy had been deduced. Biometric recognition experiments were performed finally to verify the theory deduction results. It is significant to future research on multimodal biometric and provides basis for developing multibiometric systems.

Robust real-time and rotation-invariant american sign language alphabet recognition using range camera

Abstract: The automatic interpretation of human gestures can be used for a natural interaction with computers without the use of mechanical devices such as keyboards and mice. The recognition of hand postures have 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 is to design a rotation-invariant recognition process while using a 3D signature for classifying hand postures. An heuristic and voxelbased 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 and the tracking procedure have been successfully evaluated. This study has demonstrated the efficiency of the proposed rotation invariant 3D hand posture signature which leads to 98.24% recognition rate after testing 12723 samples of 12 gestures taken from the alphabet of the American Sign Language.