Signature recognition

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

Retina biometric recognition in moving video stream using visible spectrum approach

Abstract: Biometric authentication comes in play to release the users from the difficulties of remembering and protecting passwords as required by traditional authentication systems. Among all the biometrics in use today, eye biometrics (iris and retina) offers the highest level of uniqueness, universality, permanence, and accuracy. Video-based retina recognition has been one of the hot topics in the field of pattern recognition in the last several decades. The main advantage of the video based retina recognition method is that more information is available in a video sequence than in a single image.

Method for the presentation of biometric data of a signature

Abstract: In the field of electronics specialized tablets are used to capture signatures, capable of registering, in addition to the shape of the signature, its “biometric data”: the instantaneous speed of the stylus and the instantaneous pressure exerted on the tip. A human can perfectly see and recognize in minimum details the shape of the signature, but cannot evaluate at the same time also instantaneous speed and pressure data; the method, subject of the present invention, solves the problem due to the presentation at the same time of the shape of the signature, of instantaneous pressure and of instantaneous speed, being based on the creation of a three-dimensional graphical representation of the signature with an immediate optical effect, where the signature appears as a three-dimensional solid. Even for an operator without experience in the graphological field it is easy to achieve a quite reliable judgment about the validity of a signature, by comparing its three-dimensional image with that of a reference signature, also three-dimensional.

An Empirical Study of Dimensionality Reduction Methods for Biometric Recognition

Abstract: This research aims at studying the recognition accuracy and execution time that are affected by different dimensionality reduction methods applied to the biometric image data. We comparatively study the fingerprint, face images, and handwritten signature data that are pre-processed with the two statistical based dimensionality reduction methods: principal component analysis (PCA) and linear discriminant analysis (LDA). The algorithm that has been used to train and recognize the images is support vector machine with linear and polynomial kernel functions. Experimental results showed that the application of LDA dimensionality reduction method before recognizing the image patterns with a linear kernel function of SVM is more accurate and takes less time than the recognition that did not use dimensionality reduction. LDA is a suitable technique for physiological biometrics, whereas PCA is appropriate for the behavioral biometrics. We also found out that only 1% of transformed dimensions is adequate for the accurate recognition of biometric image patterns.

Improvements in feature vector selection and parameter optimisation for continuous gesture recognition

Abstract: Gesture recognition has attracted significant interest due to diverse potential applications, including: hand writing recognition, robot control and human-computer interfaces. This paper identifies and addresses three shortcomings in current approaches to feature vector selection and parameter optimisation for continuous gesture recognition. First, in selecting the final feature vector, researchers typically analyse only a small subset of possible feature combinations; however, the limited subset is likely to omit the optimum feature vector. Second, selection of the final feature vector is based on performance in isolated recognition; however, the final feature vector may not perform adequately in continuous recognition. No protocol currently exists to evaluate and select the final feature vector in continuous recognition mode, thus a novel scoring system is developed. Finally, optimisation of the number of states in the Hidden Markov Models (HMMs) and the number of clusters (k-means clustering) is performed independently, ignoring any possible interdependency. To investigate and address these shortcomings, a gesture recognition system geared towards sign language interpretation is designed. The system is tested on a 9-word gesture vocabulary, and subsequent analysis confirms the above conjectures: first, the optimum feature vector cannot be intuitively predicted and must be determined through rigorous analysis; second, selecting the final feature vector in continuous mode improved the accuracy score by 5.85 % and the perfect sentence recognition by 47.2 %; finally, optimising the number of states and number of clusters simultaneously improved the accuracy score by 3.0 % and the perfect sentence recognition by 11.1%.

Optimized template trees for appearance based object recognition

Abstract: Appearance-based 3D object recognition is becoming popular for the recognition of faces and unstructured objects in new applications, such as mobile service robots in health care environments. Typical recognition problems are a cluttered background, the need of real-time recognition for in-the-loop applications, like object recognition and grasping by a robot, and a large search area, often the full camera image. In our approach a search window is shifted across the image in a way similar to cross-correlation and after each shift the content of the search window is classified by use of a template tree. To achieve real-time capability, adaptation of template resolution and step-size between consecutive search windows are integrated within the tree structure. The robustness of the tree classifier is significantly increased by optimization of the classifiers within the tree nodes by use of information theory methods.