Offline Handwritten Signature Verification - Literature Review

The area of Handwritten Signature Verification has been broadly researched in the last decades, but remains an open research problem. The objective of signature verification systems is to discriminate if a given signature is genuine (produced by the claimed individual), or a forgery (produced by an impostor). This has demonstrated to be a challenging task, in particular in the offline (static) scenario, that uses images of scanned signatures, where the dynamic information about the signing process is not available. Many advancements have been proposed in the literature in the last 5-10 years, most notably the application of Deep Learning methods to learn feature representations from signature images. In this paper, we present how the problem has been handled in the past few decades, analyze the recent advancements in the field, and the potential directions for future research.Comment: Accepted to the International Conference on Image Processing Theory, Tools and Applications (IPTA 2017

Non-contact Microelectronic Device Inspection Systems And Methods

Non-contact microelectronic device inspection systems and methods are discussed and provided. Some embodiments include a method of generating a virtual reference device (or chip). This approach uses a statistics to find devices in a sample set that are most similar and then averages their time domain signals to generate the virtual reference. Signals associated with the virtual reference can then be correlated with time domain signals obtained from the packages under inspection to obtain a quality signature. Defective and non-defective devices are separated by estimating a beta distribution that fits a quality signature histogram of inspected packages and determining a cutoff threshold for an acceptable quality signature. Other aspects, features, and embodiments are also claimed and described.Georgia Tech Research Corporatio

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

Differential Microlensing Measurements of Quasar Broad Line Kinematics in Q2237+0305

The detailed workings of the central engines of powerful quasars remain a mystery. This is primarily due to the fact that, at their cosmological distances, the inner regions of these quasars are spatially unresolvable. Reverberation mapping is now beginning to unlock the physics of the Broad Emission Line Region (BELR) in nearby, low-luminosity quasars, however it is still unknown whether this gas is dominated by virial motion, by outflows, or infall. The challenge is greater for more distant, powerful sources due to the very long response time of the BELR to changes in the continuum. We present a new technique for probing the kinematic properties of the BELR and accretion disk of high-z quasars using differential microlensing, and show how substantial information can be gained through a single observation of a strongly-lensed quasar using integral field spectroscopy. We apply this technique to GMOS IFU observations of the multiply-imaged quasar Q2237+0305, and find that the observed microlensing signature in the CIII] broad emission line favours gravitationally-dominated dynamics over an accelerating outflow.Comment: 16 pages, 14 figure