Anthropometry of the head and face

In this paper, we present the computational tools and a hardware prototype for 3D face recognition. Full automation is provided through the use of advanced multistage alignment algorithms, resilience to facial expressions by employing a deformable model framework, and invariance to 3D capture devices through suitable preprocessing steps. In addition, scalability in both time and space is achieved by converting 3D facial scans into compact metadata. We present our results on the largest known, and now publicly available, face recognition grand challenge 3D facial database consisting of several thousand scans. To the best of our knowledge, this is the highest performance reported on the FRGC v2 database for the 3D modality

/pdf/three-dimensional-face-recognition-in-the-presence-of-facial-5270c3dnbi.pdf

Three-Dimensional Face Recognition in the Presence of Facial Expressions: An Annotated Deformable Model Approach

We propose a new approach to image denoising, based on the image-domain minimization of an estimate of the mean squared error-Stein's unbiased risk estimate (SURE). Unlike most existing denoising algorithms, using the SURE makes it needless to hypothesize a statistical model for the noiseless image. A key point of our approach is that, although the (nonlinear) processing is performed in a transformed domain-typically, an undecimated discrete wavelet transform, but we also address nonorthonormal transforms-this minimization is performed in the image domain. Indeed, we demonstrate that, when the transform is a ldquotightrdquo frame (an undecimated wavelet transform using orthonormal filters), separate subband minimization yields substantially worse results. In order for our approach to be viable, we add another principle, that the denoising process can be expressed as a linear combination of elementary denoising processes-linear expansion of thresholds (LET). Armed with the SURE and LET principles, we show that a denoising algorithm merely amounts to solving a linear system of equations which is obviously fast and efficient. Quite remarkably, the very competitive results obtained by performing a simple threshold (image-domain SURE optimized) on the undecimated Haar wavelet coefficients show that the SURE-LET principle has a huge potential.

/pdf/the-sure-let-approach-to-image-denoising-3kk3l0nqg1.pdf

The SURE-LET Approach to Image Denoising

A qualitative and quantitative review of diffusion tensor imaging studies in reading and dyslexia

Important requirements for automotive radar are high resolution, low hardware cost, and small size. Multiple-input, multiple-output (MIMO) radar technology has been receiving considerable attention from automotive radar manufacturers because it can achieve a high angular resolution with relatively small numbers of antennas. For that ability, it has been exploited in the current-generation automotive radar for advanced driverassistance systems (ADAS) as well as in next-generation highresolution imaging radar for autonomous driving. This article reviews MIMO radar basics, highlighting the features that make this technology a good fit for automotive radar and reviewing important theoretical results for increasing the angular resolution. The article also describes challenges arising during the application of existing MIMO radar theory to automotive radar that provide interesting problems for signal processing researchers.

MIMO Radar for Advanced Driver-Assistance Systems and Autonomous Driving: Advantages and Challenges

The design of radar waveforms has received considerable attention since the 1950s. In 1953, P.M. Woodward (1953; 1953) defined the narrowband radar ambiguity function or, simply, ambiguity function. It is a device formulated to describe the effects of range and Doppler on matched filter receivers. Woodward acknowledged the influence that Shannon's communication theory from 1948 had on his ideas; and he explained the relevance of ambiguity in radar signal processing, perhaps best conceived in terms of a form of the uncertainty principle (see the sections "Motivation" and "Ambiguity Functions"). However, in the 50 or so years since Woodward's book was published, radar signal processing has used the ambiguity function as an intricate and flexible tool in the design of waveforms to solve diverse problems in radar. In the process, substantial connections were established in mathematics, physics, and other areas of signal processing. As such, we are introducing two new methods, discussed in sections "CAZAC Sequences" and "Aperiodic Simulations".

Phase-Coded Waveforms and Their Design

We present a general mathematical theory for lifting frames that allows us to modify existing filters to construct new ones that form Parseval frames. We apply our theory to design nonseparable Parseval frames from separable (tensor) products of a piecewise linear spline tight frame. These new frame systems incorporate the weighted average operator, the Sobel operator, and the Laplacian operator in directions that are integer multiples of 45/spl deg/. A new image denoising algorithm is then proposed, tailored to the specific properties of these new frame filters. We demonstrate the performance of our algorithm on a diverse set of images with very encouraging results.

Image denoising using a tight frame

It is becoming increasingly important to be able to credential and identify authorized personnel at key points of entry. Such identity management systems commonly employ biometric identifiers. In this paper, we present a novel multimodal facial recognition approach that employs data from both visible spectrum and thermal infrared sensors. Data from multiple cameras is used to construct a three-dimensional mesh representing the face and a facial thermal texture map. An annotated face model with explicit two-dimensional parameterization (UV) is then fitted to this data to construct: 1) a three-channel UV deformation image encoding geometry, and 2) a one-channel UV vasculature image encoding facial vasculature. Recognition is accomplished by comparing: 1) the parametric deformation images, 2) the parametric vasculature images, and 3) the visible spectrum texture maps. The novelty of our work lies in the use of deformation images and physiological information as means for comparison. We have performed extensive tests on the Face Recognition Grand Challenge v1.0 dataset and on our own multimodal database with very encouraging results.

Multimodal face recognition: combination of geometry with physiological information

CAZAC (Constant Amplitude Zero Autocorrelation) sequences are important in waveform design because of their optimal transmission efficiency and tight time localization properties. Certain classes of CAZAC sequences have been used in Radar processing for many years while recently discovered sequences invite further study. This paper compares several classes of CAZAC sequences with respect to both the periodic and aperiodic ambiguity function. Some computational results for different CAZAC classes are presented. In particular, we note the fact that so-called Bjorck CAZACs have sidelobes at different locations when different shifts are considered. We take advantage of this fact by using an averaging technique to lower sidelobe levels.

https://www.ese.wustl.edu/~nehorai/DARPA/publications/Radar07_Kebo.pdf

Ambiguity and sidelobe behavior of CAZAC coded waveforms

When tracking targets in radar, the selection of the transmitted waveform and the method of processing the return signal are two of the design aspects that affect measurement accuracy. Increased measurement accuracy results in enhanced tracking performance. In this paper, we apply sequential Monte Carlo methods to propose matched filtering operations in the delay-Doppler space where a target is expected to exist. Moreover, in the case of thresholding the measurements, these methods are used to form resolution cells that have the shape of the probability of detection contour. These methods offer an advantage over traditional radar tracking methods that form tessellating resolution cells to approximate the probability of detection contours, and exhaustively perform matched filtering operations over the entire delay-Doppler space. With the use of a Bjorck constant amplitude zero-autocorrelation (CAZAC) sequence, a high resolution measurement is attained and the use of thresholding is avoided. This is an advantage over commonly used waveforms such as linear frequency modulated chirps (LFMs). We examine the properties of Bjorck CAZACs and demonstrate improved tracking performance over LFMs in a single target tracking scenario.

https://www.ese.wustl.edu/~nehorai/MURI/publications/WDD07_Kyriakides.pdf

I. Konstantinidis

Papers

Phase-Coded Waveforms and Their Design

Image denoising using a tight frame

Multimodal face recognition: combination of geometry with physiological information

Ambiguity and sidelobe behavior of CAZAC coded waveforms

Target tracking using particle filtering and CAZAC sequences