12,892 research outputs found
Analysis of pattern recognition techniques for in-air signature biometrics
As a result of advances in mobile technology, new services which benefit from the ubiquity of these devices are appearing. Some of these services require the identification of the subject since they may access private user information. In this paper, we propose to identify each user by drawing his/her handwritten signature in the air (in-airsignature). In order to assess the feasibility of an in-airsignature as a biometric feature, we have analysed the performance of several well-known patternrecognitiontechniques—Hidden Markov Models, Bayes classifiers and dynamic time warping—to cope with this problem. Each technique has been tested in the identification of the signatures of 96 individuals. Furthermore, the robustness of each method against spoofing attacks has also been analysed using six impostors who attempted to emulate every signature. The best results in both experiments have been reached by using a technique based on dynamic time warping which carries out the recognition by calculating distances to an average template extracted from several training instances. Finally, a permanence analysis has been carried out in order to assess the stability of in-airsignature over time
Three-dimensional face recognition: An Eigensurface approach
We evaluate a new approach to face recognition using a variety of surface representations of three-dimensional facial structure. Applying principal component analysis (PCA), we show that high levels of recognition accuracy can be achieved on a large database of 3D face models, captured under conditions that present typical difficulties to more conventional two-dimensional approaches. Applying a ran-c of image processing, techniques we identify the most effective surface representation for use in such application areas as security surveillance, data compression and archive searching
Detecting agricultural to urban land use change from multi-temporal MSS digital data
Conversion of agricultural land to a variety of urban uses is a major problem along the Wasatch Front, Utah. Although LANDSAT MSS data is a relatively coarse tool for discriminating categories of change in urban-size plots, its availability prompts a thorough test of its power to detect change. The procedures being applied to a test area in Salt Lake County, Utah, where the land conversion problem is acute are presented. The identity of land uses before and after conversion was determined and digital procedures for doing so were compared. Several algorithms were compared, utilizing both raw data and preprocessed data. Verification of results involved high quality color infrared photography and field observation. Two data sets were digitally registered, specific change categories internally identified in the software, results tabulated by computer, and change maps printed at 1:24,000 scale
On the kinematic signature of a central Galactic bar in observed star samples
A quasi self-consistent model for a barred structure in the central regions
of our Galaxy is used to calculate the signature of such a triaxial structure
on the kinematical properties of star samples. We argue that, due to the
presence of a velocity dispersion, such effects are much harder to detect in
the stellar component than in the gas. It might be almost impossible to detect
stellar kinematical evidence for a bar using only l-v diagrams, if there is no
a priori knowledge of the potential. Therefore, we propose some test parameters
that can easily be applied to observed star samples, and that also incorporate
distances or proper motions. We discus the diagnostic power of these tests as a
function of the sample size and the bar strength. We conclude that about 1000
stars would be necessary to diagnose triaxiality with some statistical
confidence.Comment: 9 pages + 8 PS figures, uses aas2pp4.sty. Accepted by Ap
3D Face Recognition: Feature Extraction Based on Directional Signatures from Range Data and Disparity Maps
In this paper, the author presents a work on i) range data and ii) stereo-vision system based disparity map profiling that are used as signatures for 3D face recognition. The signatures capture the intensity variations along a line at sample points on a face in any particular direction. The directional signatures and some of their combinations are compared to study the variability in recognition performances. Two 3D face image datasets namely, a local student database captured with a stereo vision system and the FRGC v1 range dataset are used for performance evaluation
Dynamic user authentication based on mouse movements curves
In this paper we describe a behavioural biometric approach to authenticate users dynamically based on mouse movements only and using regular mouse devices. Unlike most of the previous approaches in this domain, we focus here on the properties of the curves generated from the consecutive mouse positions during typical mouse movements. Our underlying hypothesis is that these curves have enough discriminative information to recognize users. We conducted an experiment to test and validate our model in which ten participants are involved. Back propagation neural network is used as a classifier. Our experimental results show that behavioural information with discriminating features is revealed during normal mouse usage, which can be employed for user modeling for various reasons, such as information assets protection
Dense 3D Face Correspondence
We present an algorithm that automatically establishes dense correspondences
between a large number of 3D faces. Starting from automatically detected sparse
correspondences on the outer boundary of 3D faces, the algorithm triangulates
existing correspondences and expands them iteratively by matching points of
distinctive surface curvature along the triangle edges. After exhausting
keypoint matches, further correspondences are established by generating evenly
distributed points within triangles by evolving level set geodesic curves from
the centroids of large triangles. A deformable model (K3DM) is constructed from
the dense corresponded faces and an algorithm is proposed for morphing the K3DM
to fit unseen faces. This algorithm iterates between rigid alignment of an
unseen face followed by regularized morphing of the deformable model. We have
extensively evaluated the proposed algorithms on synthetic data and real 3D
faces from the FRGCv2, Bosphorus, BU3DFE and UND Ear databases using
quantitative and qualitative benchmarks. Our algorithm achieved dense
correspondences with a mean localisation error of 1.28mm on synthetic faces and
detected anthropometric landmarks on unseen real faces from the FRGCv2
database with 3mm precision. Furthermore, our deformable model fitting
algorithm achieved 98.5% face recognition accuracy on the FRGCv2 and 98.6% on
Bosphorus database. Our dense model is also able to generalize to unseen
datasets.Comment: 24 Pages, 12 Figures, 6 Tables and 3 Algorithm
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