Face recognition using computer-generated database
Many face database and recognition systems have been constructed in specially designed studios with various illuminations, poses, and expressions. However, none of these databases yet satisfies a large variation of poses and illuminations that permit the study of systematic 3D human face information, which results in an unsatisfactory success rate. It is caused primarily by the difficulty of data collection of facial images to satisfy the large variation of poses and illuminations to fully represent the 3D characteristics of human faces. We present how computer-generated database can be used for face recognition experiment focused on multiview face recognition/descriptor. We show multiview face data collection using rendering of 3D models. We also illustrate our approach how to build a face descriptor containing 3D information of human face using multiview concepts. This multiview face recognition descriptor is a 3D descriptor using the concept how much powerful a view influences over nearby views, so called as "quasiview " size

title: Facial shape and 3D skin
abstract: We present novel ideas for facial shape and skin simulation on extremely detailed threedimensional facial meshes. Our input database is composed of a small number of scanned human faces with resolutions up to several million triangles, where even the pores are clearly distinguished. We show how to decompose the facial meshes into the global shape of the face plus skin detail (3D skin), and then to reconstitute them. Our modeling methodology allows us to simulate the exaggeration of the facial global shape, retaining the original skin detail, as well as to transfer 3D skin from one face to another. First, we represent all the input faces in terms of a homogeneous structure on the base model in low resolution by using mesh adaptation techniques. Second, the differences between the original mesh and a base mesh, which appear as skin detail, are captured and stored, so that each face is decomposed into the global shape (a base mesh) plus skin detail. Face reconstitution after global shape exaggeration and/or skin transfer enables delicate simulation of facial models. In addition, we can increase the resolution of any model scanned at a low resolution by transferring skin from a higher resolution model. Our method shows successful manipulation of the minute structure of 3D skin differently from other methods such as Normal Mapping, Displacement Mapping, Displaced Subdivision Surfaces, and Normal Meshes where none of these techniques show manipulation of minute structure like ours and only approximation is used while our method recovers the original structure.

title: Personalized face and speech communication over the Internet
abstract: We present our system for personalized face and speech communication over the Internet. The overall system consists of three parts: the cloning of real human faces to use as the representative avatars; the Networked Virtual Environment System performing the basic task of network and device management; and the speech system which includes a text-to-speech engine and a real time phoneme extraction engine from natural speech. The combination of these three elements provides a system to allow real humans, represented by their virtual counterparts, to communicate with each other even when they are geographically remote. In addition to this, all elements present use MPEG-4 as a common communication and animation standard and were designed and tested on the Windows operating system (OS). The paper presents the main aim of the work, the methodology and the resulting communication system.

title: Real Face Communication in a Virtual World
abstract: This paper describes an efficient method to make an individual face for animation from several possible inputs and how to use this result for a realistic talking head communication in a virtual world. We present a method to reconstruct 3D facial model from two orthogonal pictures taken from front and side views. The method is based on extracting features from a face in a semiautomatic way and deforming a generic model. Texture mapping based on cylindrical projection is employed using a composed image from the two images. A reconstructed head is animated immediately and is able to talk with given text, which is transformed to corresponding phonemes and visemes. We also propose a system for individualized face-to-face communication through network using MPEG4.

title: Simulating Virtual Humans in Networked Virtual Environments
abstract: In the past decade, networked virtual environments (NVEs) have been an increasingly active area of research, with the first commercial systems emerging recently. Graphical and behavioral representation of users within such systems is a particularly important issue that has lagged in development behind other issues such as network architectures and space structuring. In this paper, we expose the importance of using virtual humans within these systems and provide a brief overview of several virtual humans technologies used in particular for simulation of crowds. As the main technical contribution, the paper presents the integration of these technologies with the COVEN-DIVE platform, the extension of the DIVE system developed within the COVEN project. In conjunction with this, we present our contributions through the COVEN project to the MPEG-4 standard concerning the representation of virtual humans.

title: Generating Animatable 3D Virtual Humans from Photographs
abstract: We present an easy, practical and efficient full body cloningmethodology. This system utilizes photos taken from the front, side and back of a person in any given imaging envir onment without requiring a special background or a controlled illuminating condition. A seamless generic bo dy specified in the VRML H-Anim 1.1 format is used to generate an individualized virtual human. The system is c of two major components: face-cloning and body-cloning. The face-cloning component uses feature poi nts on front and side images and then applies DFFD for shape modification. Next a fully automatic seamless text ure mapping is generated for 360 o coloring on a 3D polygonal model. The body-cloning component has two step s: (i) feature points specification, which enables automatic silhouette detection in an arbitrary background(ii) two-stage body modification by using feature points and body silhouette respectively. The final integrated huma n model has photo-realistic animatable face, hands, feet and body. The result can be visualized in any VRML compli ant browser.

title: HoneyStat: Local Worm Detection Using Honeypots
abstract: Worm detection systems have traditionally used global strategies and focused on scan rates. The noise associated with this approach requires statistical techniques and large data sets (e.g., 220 monitored machines) to yield timely alerts and avoid false positives. Worm detection techniques for smaller local networks have not been fully explored.

title: Worm detection, early warning and response based on local victim information
abstract: Worm detection systems have traditionally focused on global strategies. In the absence of a global worm detection system, we examine the effectiveness of local worm detection and response strategies. This paper makes three contributions: (1) we propose a simple two-phase local worm victim detection algorithm, DSC (Destination-Source Correlation), based on worm behavior in terms of both infection pattern and scanning pattern. DSC can detect zero-day scanning worms with a high detection rate and very low false positive rate. (2) We demonstrate the effectiveness of early worm warning based on local victim information. For example, warning occurs with 0.19% infection of all vulnerable hosts on Internet when using a /12 monitored network. (3) Based on local victim information, we investigate and evaluate the effectiveness of an automatic real-time local response in terms of slowing down the global Internet worms propagation. (2) and (3) are general results, not specific to certain detection algorithm like DSC. We demonstrate (2) and (3) with both analytical models and packet-level network simulator experiments.

title: Lower bounds on the VC-dimension of smoothly parametrized function classes
abstract: We examine the relationship between the VC-dimension and the number of parameters of a smoothly parametrized function class. We show that the VC-dimension of such a function class is at least k if there exists a k-dimensional differentiable manifold in the parameter space such that each member of the manifold corresponds to a different decision boundary. Using this result, we are able to obtain lower bounds on the VC-dimension proportional to the number of parameters for several function classes including two-layer neural networks with certain smooth activation functions and radial basis functions with a gaussian basis. These lower bounds hold even if the magnitudes of the parameters are restricted to be arbitarily small. In Valiant's probably approximately correct learning framework, this implies that the number of example necessary for learning these function classes is at least linear in the number of parameters.

title: Algorithms for computing the sparsest shifts of polynomials via the Berlekamp/Massey algorithm
abstract: As a sub-procedure our algorithm executes the Berlekamp/Massey algorithm on a sequence of large integers or polynomials. We give a fraction-free version of the Berlekamp/Massey algorithm, which does not require rational numbers or functions and GCD operations on the arising numerators and denominators. The relationship between the solution of Toeplitz systems, Padé approximations, and the Euclidean algorithm is classical. Fraction-free versions  can be obtained from the subresultant PRS algorithm . Dornstetter  gives an interpretation of the Berlekamp/Massey algorithm as a partial extended Euclidean algorithm. We map the subresultant PRS algorithm onto Dornstetter's formulation. We note that the Berlekamp/Massey algorithm is more efficient than the classical extended Euclidean algorithm.

title: Mining in a data-flow environment: experience in network intrusion detection
abstract: We discuss the KDD process in “data-flow” environments, where unstructured and time dependent data can be processed into various levels of structured and semanticallyrich forms for analysis tasks. Using network intrusion detection as a concrete application example, we describe how to construct models that are both acczLrate in describing the underlying concepts, and efficient when used to analyze data in real-time. We present procedures for analyzing frequent patterns from lower level data and constructing appropriate features to formulate higher level data. The features generated from various levels of data have different computational costs (in time and space). We show that in order to minimize the time required in using the classification models in a real-time environment, we can exploit the “necessary conditions” associated with the lowcost features to determine whether some high-cost features need to be computed and the corresponding classification rules need to be checked. We have applied our tools to the problem of building network intrusion detection models. We report our experiments using the network data provided as part of the 1998 DARPA Intrusion Detection Evaluation program. We also discuss our experience in using the mined models in NFR, a real-time network intrusion detection system.