240,349 research outputs found
Visual identification by signature tracking
We propose a new camera-based biometric: visual signature identification. We discuss the importance of the parameterization of the signatures in order to achieve good classification results, independently of variations in the position of the camera with respect to the writing surface. We show that affine arc-length parameterization performs better than conventional time and Euclidean arc-length ones. We find that the system verification performance is better than 4 percent error on skilled forgeries and 1 percent error on random forgeries, and that its recognition performance is better than 1 percent error rate, comparable to the best camera-based biometrics
Feature Representation for Online Signature Verification
Biometrics systems have been used in a wide range of applications and have
improved people authentication. Signature verification is one of the most
common biometric methods with techniques that employ various specifications of
a signature. Recently, deep learning has achieved great success in many fields,
such as image, sounds and text processing. In this paper, deep learning method
has been used for feature extraction and feature selection.Comment: 10 pages, 10 figures, Submitted to IEEE Transactions on Information
Forensics and Securit
Automatic landmark annotation and dense correspondence registration for 3D human facial images
Dense surface registration of three-dimensional (3D) human facial images
holds great potential for studies of human trait diversity, disease genetics,
and forensics. Non-rigid registration is particularly useful for establishing
dense anatomical correspondences between faces. Here we describe a novel
non-rigid registration method for fully automatic 3D facial image mapping. This
method comprises two steps: first, seventeen facial landmarks are automatically
annotated, mainly via PCA-based feature recognition following 3D-to-2D data
transformation. Second, an efficient thin-plate spline (TPS) protocol is used
to establish the dense anatomical correspondence between facial images, under
the guidance of the predefined landmarks. We demonstrate that this method is
robust and highly accurate, even for different ethnicities. The average face is
calculated for individuals of Han Chinese and Uyghur origins. While fully
automatic and computationally efficient, this method enables high-throughput
analysis of human facial feature variation.Comment: 33 pages, 6 figures, 1 tabl
Multistatic human micro-Doppler classification of armed/unarmed personnel
Classification of different human activities using multistatic micro-Doppler data and features is considered in this paper, focusing on the distinction between unarmed and potentially armed personnel. A database of real radar data with more than 550 recordings from 7 different human subjects has been collected in a series of experiments in the field with a multistatic radar system. Four key features were extracted from the micro-Doppler signature after Short Time Fourier Transform analysis. The resulting feature vectors were then used as individual, pairs, triplets, and all together before inputting to different types of classifiers based on the discriminant analysis method. The performance of different classifiers and different feature combinations is discussed aiming at identifying the most appropriate features for the unarmed vs armed personnel classification, as well as the benefit of combining multistatic data rather than using monostatic data only
Feature diversity for optimized human micro-doppler classification using multistatic radar
This paper investigates the selection of different combinations of features at different multistatic radar nodes, depending on scenario parameters, such as aspect angle to the target and signal-to-noise ratio, and radar parameters, such as dwell time, polarisation, and frequency band. Two sets of experimental data collected with the multistatic radar system NetRAD are analysed for two separate problems, namely the classification of unarmed vs potentially armed multiple personnel, and the personnel recognition of individuals based on walking gait. The results show that the overall classification accuracy can be significantly improved by taking into account feature diversity at each radar node depending on the environmental parameters and target behaviour, in comparison with the conventional approach of selecting the same features for all nodes
- …