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.

/pdf/eigenfaces-vs-fisherfaces-recognition-using-class-specific-2qyevb22n0.pdf

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.

/pdf/computer-vision-algorithms-and-applications-25dn6wu83j.pdf

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.

/pdf/detecting-faces-in-images-a-survey-4yhbbns6mr.pdf

Detecting faces in images: a survey

The present technique addressably handles image data, which is decomposed and tessellated into a plurality of tessellated sub-band blocks. The tessellated sub-band blocks may be addressed by an array of indices, which identify specific data blocks of the image data by decomposition level and spatial coordinates of the tessellated sub-band blocks. Accordingly, a desired region of the image data at multiple resolutions can be identified and individually handled for storage, transmission, retrieval, and display. The desired region also can be reference marked based on the array of indices.

Image tessellation for region-specific coefficient access

A method for managing medical image data transmission between computing devices is disclosed. In one embodiment, the method includes monitoring a plurality of parameters of a computer network that includes a server and a client. The plurality of parameters may include a client resource parameter, a server resource parameter, and a network operating parameter. The disclosed method may also include automatically determining a desired compression level at which to send medical image data to the client based at least in part on the client resource parameter, the server resource parameter, and the network operating parameter. Further, in one embodiment the method may include communicating the medical image data from the server to the client at the desired compression level in response to a client request for the image data. Various other methods, systems, and manufactures are also disclosed.

Networked image visualization image quality enhancement method and system

An image display system includes user configurable viewports for the review and analysis of image data. The user configurable viewports include a review viewport and an analysis viewport. The review viewport displays in a review mode, a plurality of image views of the image data. The analysis viewport displays in an analysis mode, a plurality of image views of the image data. The review viewport and the analysis viewport may be configured for simultaneous display of related image data.

Clinical review and analysis work flow for lung nodule assessment

A data processing technique is provided. In one embodiment, a computer-implemented method includes receiving a set of dynamic computed tomography image data from a computed tomography imaging system, registering the image data, applying a smoothing filter to at least a selection of the registered image data, and outputting the results. The smoothing filter may be, for example, a uniform filter, a triangular filter, a Gaussian filter, a median filter, a percentile filter, a Kuwahara filter, an anisotropic filter, or any combination thereof. Additional methods, systems, and devices are also disclosed.

Smoothing of dynamic data sets

Systems, methods and apparatus are provided through which in some embodiments, user interactions of adding, removing and selecting scale, within a mesh framework to improve repeatability and reproducibility of an arbitrary process in a three-dimensional medical space. In some embodiments, a multiple-mesh framework further improves performance of an arbitrary segmentation process.

Saad Ahmed Sirohey

Papers

Image tessellation for region-specific coefficient access

Networked image visualization image quality enhancement method and system

Clinical review and analysis work flow for lung nodule assessment

Smoothing of dynamic data sets

Systems, methods and apparatus for segmentation of data involving a hierarchical mesh