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

Continuous dynamic time warping for translation-invariant curve alignment with applications to signature verification

The problem of establishing correspondence and measuring the similarity of a pair of planar curves arises in many applications in computer vision and pattern recognition. This paper presents a new method for comparing planar curves and for performing matching at sub-sampling resolution. The analysis of the algorithm as well as its structural properties are described. The performance of the new technique applied to the problem of signature verification is shown and compared with the performance of the well-known Dynamic Time Warping algorithm

Visual input for pen-based computers

The design and implementation of a camera-based, human-computer interface for acquisition of handwriting is presented. The camera focuses on a standard sheet of paper and images a common pen; the trajectory of the tip of the pen is tracked and the contact with the paper is detected. The recovered trajectory is shown to have sufficient spatio-temporal resolution and accuracy to enable handwritten character recognition. More than 100 subjects have used the system and have provided a large and heterogeneous set of examples showing that the system is both convenient and accurate

Visual signature verification using affine arc-length

Signatures can be acquired with a camera-based system with enough resolution to perform verification. This paper presents the performance of a visual-acquisition signature verification system, emphasizing on the importance of the parameterisation of the signature in order to achieve good classification results. A technique to overcome the lack of examples in order to estimate the generalization error of the algorithm is also described

Monocular graph SLAM with complexity reduction

Abstract-We present a graph-based SLAM approach, using monocular vision and odometry, designed to operate on computationally constrained platforms. When computation and memory are limited, visual tracking becomes difficult or impossible, and map representation and update costs must remain low. Our system constructs a map of structured views using only weak temporal assumptions, and performs recognition and relative pose estimation over the set of views. Visual observations are fused with differential sensors in an incrementally optimized graph representation. Using variable elimination and constraint pruning, the graph complexity and storage is kept linear in explored space rather than in time. We evaluate performance on sequences with ground truth, and also compare to a standard graph SLAM approach

View management for lifelong visual maps

The time complexity of making observations and loop closures in a graph-based visual SLAM system is a function of the number of views stored. Clever algorithms, such as approximate nearest neighbor search, can make this function sub-linear. Despite this, over time the number of views can still grow to a point at which the speed and/or accuracy of the system becomes unacceptable, especially in computation- and memory-constrained SLAM systems. However, not all views are created equal. Some views are rarely observed, because they have been created in an unusual lighting condition, or from low quality images, or in a location whose appearance has changed. These views can be removed to improve the overall performance of a SLAM system. In this paper, we propose a method for pruning views in a visual SLAM system to maintain its speed and accuracy for long term use.Comment: IEEE International Conference on Intelligent Robots and Systems (IROS), 201

Bayesian Subspace Methods For Acoustic Signature Recognition Of Vehicles

Vehicles may be recognized from the sound they make when moving, i.e., from their acoustic signature. Characteristic patterns may be extracted from the Fourier description of the signature and used for recognition. This paper compares conventional methods used for speaker recognition, namely, systems based on Mel-frequency cepstral coefficients (MFCC) and either Gaussian mixture models (GMM) or hidden Markov models (HMM), with Bayesian subspace method based on the short term Fourier transform (STFT) of the vehicles' acoustic signature. A probabilistic subspace classifier achieves a 11.7% error for the ACIDS database, outperforming conventional MFCC-GMM- and MFCC-HMM-based systems by 50%. 1

Visual Input for Pen-Based Computers

Handwriting may be captured using a video camera, rather than the customary pressure-sensitive tablet. This paper presents a simple system based on correlation and recursive prediction methods that can track the tip of the pen in real time with sufficient spatio-temporal resolution and accuracy to enable handwritten character recognition. The system is tested on a large and heterogeneous set of examples and its performance is compared to that of three human operators and a commercial high-resolution pressure-sensitive tablet. Keywords: Systems and applications, Active and real-time vision, Handwriting acquisition. 1 Introduction and Motivation Computers are getting faster and smaller every day. Notebook and laptop personal computers, pen-based computers and personal organizers, are designed to be as small and portable as possible. While until now their size was limited by hard disk, memory chips, battery and power supplies, the lower bound is now increasingly dependent on the size o..

Camera-based ID Verification by Signature Tracking

. A number of vision-based biometric techniques have been proposed in the past for personal identification. We present a novel one based on visual capturing of signatures. This paper describes a system based on correlation and recursive prediction methods that can track the tip of the pen in real time, with sufficient spatio-temporal resolution and accuracy to enable signature verification. Several examples and the performance of the system are shown. 1 Introduction and Motivation A number of biometric techniques have been proposed for personal identification in the past. Among the vision-based ones, we can mention face recognition [21], [22], [23], fingerprint recognition [6], iris scanning [4] and retina scanning. Voice recognition or signature verification are the most widely known among the non-vision based ones. Signature verification requires the use of electronic tablets or digitizers for on-line capturing and optical scanners for off-line conversion [20]. These interfaces have..