We develop a face recognition algorithm which is insensitive to large variation in lighting direction and facial expression. Taking a pattern classification approach, we consider each pixel in an image as a coordinate in a high-dimensional space. We take advantage of the observation that the images of a particular face, under varying illumination but fixed pose, lie in a 3D linear subspace of the high dimensional image space-if the face is a Lambertian surface without shadowing. However, since faces are not truly Lambertian surfaces and do indeed produce self-shadowing, images will deviate from this linear subspace. Rather than explicitly modeling this deviation, we linearly project the image into a subspace in a manner which discounts those regions of the face with large deviation. Our projection method is based on Fisher's linear discriminant and produces well separated classes in a low-dimensional subspace, even under severe variation in lighting and facial expressions. The eigenface technique, another method based on linearly projecting the image space to a low dimensional subspace, has similar computational requirements. Yet, extensive experimental results demonstrate that the proposed "Fisherface" method has error rates that are lower than those of the eigenface technique for tests on the Harvard and Yale face databases.

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Eigenfaces vs. Fisherfaces: recognition using class specific linear projection

As one of the most successful applications of image analysis and understanding, face recognition has recently received significant attention, especially during the past several years. At least two reasons account for this trend: the first is the wide range of commercial and law enforcement applications, and the second is the availability of feasible technologies after 30 years of research. Even though current machine recognition systems have reached a certain level of maturity, their success is limited by the conditions imposed by many real applications. For example, recognition of face images acquired in an outdoor environment with changes in illumination and/or pose remains a largely unsolved problem. In other words, current systems are still far away from the capability of the human perception system.This paper provides an up-to-date critical survey of still- and video-based face recognition research. There are two underlying motivations for us to write this survey paper: the first is to provide an up-to-date review of the existing literature, and the second is to offer some insights into the studies of machine recognition of faces. To provide a comprehensive survey, we not only categorize existing recognition techniques but also present detailed descriptions of representative methods within each category. In addition, relevant topics such as psychophysical studies, system evaluation, and issues of illumination and pose variation are covered.

Face recognition: A literature survey

We present a generative appearance-based method for recognizing human faces under variation in lighting and viewpoint. Our method exploits the fact that the set of images of an object in fixed pose, but under all possible illumination conditions, is a convex cone in the space of images. Using a small number of training images of each face taken with different lighting directions, the shape and albedo of the face can be reconstructed. In turn, this reconstruction serves as a generative model that can be used to render (or synthesize) images of the face under novel poses and illumination conditions. The pose space is then sampled and, for each pose, the corresponding illumination cone is approximated by a low-dimensional linear subspace whose basis vectors are estimated using the generative model. Our recognition algorithm assigns to a test image the identity of the closest approximated illumination cone. Test results show that the method performs almost without error, except on the most extreme lighting directions.

From few to many: illumination cone models for face recognition under variable lighting and pose

Humans perceive the three-dimensional structure of the world with apparent ease. However, despite all of the recent advances in computer vision research, the dream of having a computer interpret an image at the same level as a two-year old remains elusive. Why is computer vision such a challenging problem and what is the current state of the art? Computer Vision: Algorithms and Applications explores the variety of techniques commonly used to analyze and interpret images. It also describes challenging real-world applications where vision is being successfully used, both for specialized applications such as medical imaging, and for fun, consumer-level tasks such as image editing and stitching, which students can apply to their own personal photos and videos. More than just a source of recipes, this exceptionally authoritative and comprehensive textbook/reference also takes a scientific approach to basic vision problems, formulating physical models of the imaging process before inverting them to produce descriptions of a scene. These problems are also analyzed using statistical models and solved using rigorous engineering techniques Topics and features: structured to support active curricula and project-oriented courses, with tips in the Introduction for using the book in a variety of customized courses; presents exercises at the end of each chapter with a heavy emphasis on testing algorithms and containing numerous suggestions for small mid-term projects; provides additional material and more detailed mathematical topics in the Appendices, which cover linear algebra, numerical techniques, and Bayesian estimation theory; suggests additional reading at the end of each chapter, including the latest research in each sub-field, in addition to a full Bibliography at the end of the book; supplies supplementary course material for students at the associated website, http://szeliski.org/Book/. Suitable for an upper-level undergraduate or graduate-level course in computer science or engineering, this textbook focuses on basic techniques that work under real-world conditions and encourages students to push their creative boundaries. Its design and exposition also make it eminently suitable as a unique reference to the fundamental techniques and current research literature in computer vision.

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Computer Vision: Algorithms and Applications

Images containing faces are essential to intelligent vision-based human-computer interaction, and research efforts in face processing include face recognition, face tracking, pose estimation and expression recognition. However, many reported methods assume that the faces in an image or an image sequence have been identified and localized. To build fully automated systems that analyze the information contained in face images, robust and efficient face detection algorithms are required. Given a single image, the goal of face detection is to identify all image regions which contain a face, regardless of its 3D position, orientation and lighting conditions. Such a problem is challenging because faces are non-rigid and have a high degree of variability in size, shape, color and texture. Numerous techniques have been developed to detect faces in a single image, and the purpose of this paper is to categorize and evaluate these algorithms. We also discuss relevant issues such as data collection, evaluation metrics and benchmarking. After analyzing these algorithms and identifying their limitations, we conclude with several promising directions for future research.

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Detecting faces in images: a survey

Certain embodiments of the present invention provide systems, methods and computer instructions for detecting a pathological condition of a vasculature. Certain embodiments provide a method for detecting a pathological condition of a vasculature. The method includes accessing imaging data indicative of the vasculature and having a data type, selecting a detection process corresponding to the data type from among a plurality of detection processes, each of the detection processes processing data of a different data type. The method also includes processing the imaging data having the data type with the selected detection process, and superimposing the processed imaging data on the imaging data indicative of the pathological condition of the vasculature.

Systems and Methods for Automated Diagnosis

The method comprises: accessing the multi-dimensional dataset; generating a plurality of differential operators for the multi-dimensional dataset using a discrete approximation of an analytic function; and forming a plurality of geometric responses based on a plurality of differential operators resultant from the generating. The method optionally further includes isolating a selected region of interest from the multi-dimensional dataset; the selected region of interest comprising a subset of the imaging volume.

Filtering and visualization of a multidimensional volumetric dataset

A technique is provided for improving workflow in processing and exchange of image data in a medical context. Image data may be accessed for processing, and save-state information may be generated following such processing that captures a state of an application used to process the image data files. A link to the save-state information may be stored on a common memory device and selected or executed by a radiologist or downstream service provider for accessing both the images and for placing the application in the state used to process the images. The resulting workflow greatly facilitates access to images as well as restoration of useful states of applications used to process the images.

Method for performing distributed analysis and interactive review of medical image data

PROBLEM TO BE SOLVED: To provide a method and a system which offer flexibility and controllability to a user of a distributed medical imaging system, by efficiently assigning medical image processing tasks over the whole distributed network. SOLUTION: Communications are carried out in the distributed network, including related system resources 308 and 316 in a server 302, a client 312, and a communication pathway 305. The system resources 308, 316, and the bandwidth are monitored by at least one process monitor 310 or 314 so as to generate monitoring data. Assignment of at least a part of the system resources 308 and 316 for processing three dimensional image data so as to create two dimensional image data which can be displayed in the client 312 is recommended, based on the monitoring data at least partially. COPYRIGHT: (C)2007,JPO&INPIT

Distributed image processing of medical image

Certain embodiments of the present invention provide a system and method for displaying a set of data with a virtually dissected anatomical structure. In an embodiment, the anatomical structure is a colon and various attributes of the colonic lumen are assigned a color. In an embodiment, a virtual dissection of the colon is created by mapping a three-dimensional data set to a two dimensional data set. A plurality of display index values are computed which correspond to the three-dimensional data set. Various colors are assigned to specific display index values. The three-dimensional display index values are mapped to a two-dimensional set of display index values. As directed by a user, various color cues may be displayed with the virtually dissected lumen to provide color highlights to various aspects of the colon, such as highlighting shape, fluid, or fecal presence.

Saad Ahmed Sirohey

Papers

Systems and Methods for Automated Diagnosis

Filtering and visualization of a multidimensional volumetric dataset

Method for performing distributed analysis and interactive review of medical image data

Distributed image processing of medical image

System and method for overlaying color cues on a virtual representation of an anatomical structure