Signature recognition

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

Human Action Recognition Using Dynamic Time Warping and Voting Algorithm (1)

Abstract: This paper presents a human action recognition method using dynamic time warping and voting algorithms on 3D human skeletal models. In this method human actions, which are the combinations of multiple body pa rt movements, are described by feature matrices concer ning both spatial and temporal domains. The feature matrices are created based on the spatial selection of relative angles between body parts in time seri es. Then, action recognition is done by applying a classifier which is the combination of dynamic time warping ( DTW) and a voting algorithm to the feature matrices. Exp erimental results show that the performance of our action recognition method obtains high recognition accurac y at reliable computation speed and can be applied in real time human action recognition systems.

Enhancing training data for handwriting recognition of whiteboard notes with samples from a different database

Abstract: Recognition of unconstrained handwritten text is still a challenge In this paper we consider a new problem, which is the recognition of notes written on a whiteboard Our recognizer is based on hidden Markov models (HMMs) As it is difficult to acquire sufficient amounts of training data for the HMMs we propose two strategies for enlarging the training set Both strategies are based on an existing database of offline handwritten text, which includes handwriting samples different from whiteboard data The two proposed strategies are MAP adaptation and merging of training sets With these methods we can achieve improvements of the word recognition rate of up to 57%

A discrete parameter HMM approach to on-line handwriting recognition

Abstract: One area where on-line handwriting recognition technology is most critical is the domain of small portable platforms Because such platforms have limited resources, it is not presently practical to consider a continuous parameterization for the hidden Markov models used in the recognition On the other hand, discrete parameter techniques such as used in speech recognition are difficult to apply, because there is no well-understood handwriting equivalent to phonological rules A possible solution is to extract this information directly from the data, by constructing an alphabet of sub-character, elementary handwriting units The performance of this method is illustrated on a discrete handwriting recognition task with an alphabet of 81 characters

Biometric Identification of Mice

Abstract: We present a new application area for biometric recognition: the identification of laboratory animals to replace today’s invasive methods. Through biometric identification a non invasive identification technique is applied with a code space that is restricted only by the uniqueness of the biometric identifier in use, and with an error rate that is predictable. In this work we present the blood vessel pattern in a mouse-ear as a suitable biometric identifier used for mouse identification. Genuine and Impostor score distributions are presented using a total of 50 mice. An EER of 2.5% is reported for images captured at the same instance of time which verifies the distinctive property of the biometric identifier.

Authentication based on a changeable biometric using gesture recognition with the Kinect

Abstract: Biometric systems either use physiological or behavioural characteristics to identify an individual. However, if a biometric is compromised it could be difficult or impossible to change it. This paper proposes a biometric authentication system based on gesture recognition, where gestures can be easily changed by the user. The system uses a Kinect™ device to capture and extract features, as it provides 20 skeleton tracking points: we use just six of these in our system. The Dynamic Time Warping (DTW) algorithm is used to find an optimal alignment between gestures which are time-bound sequences. We tested the system on a sample of 38 volunteers. Ten volunteers provided reference gestures of their own design and 28 volunteers attempted to attack these reference gestures by both guessing and copying. Guessing the gesture was unsuccessful in all cases, but when the attacker had previously seen a video of the reference gesture the experiment gave us an estimation of the True Positive Rate (TPR) of 0.93, False Positive Rate (FPR) of 0.017 and Equal Error Rate (EER) of 0.028.