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

PROBLEM TO BE SOLVED: To provide a method and system to evaluate potential occlusion inside a cardiac vessel to determine potential functional impact. SOLUTION: A method and system 10 for processing a cardiac image dataset acquired from an imaging system 10 is provided. A location of a first potential blockage point 152 within a first cardiac vessel is located. A functional impact of the first potential blockage point 152 on the cardiac tissue is predicted, and a representation of the functional impact is provided. In another embodiment, a method for creating an impact atlas for predicting impact of a blockage within a vessel comprises identifying potential blockage points within cardiac vessels of an image dataset, is provided. Impact regions are grown based on the blockage points within the cardiac vessels. Each of the blockage points is associated with at least one of the impact regions in an impact atlas file. COPYRIGHT: (C)2008,JPO&INPIT

Method and system for creating and using impact atlas

Es
wird ein Verfahren (1000) angegeben zur Synchronisation einander
entsprechender Landmarks auf einer Anzahl Bilder eines elastischen
Objektes. Das Verfahren beinhaltet das Identifizieren (1012) mehrerer
Landmarks in einem ersten Bild (103, 210) des Objektes und in einem
zweiten Bild (105, 220) des Objektes, das Bestimmen (1014) einer
Entsprechung zwischen den Landmarks in dem ersten Bild (103, 210)
und den Landmarks in dem zweiten Bild (105, 220), das Bestimmen (1018)
einer Abstandstransformation zwischen einem Paar einander benachbarter
Landmarks in dem ersten Bild (103, 210) und dem entsprechenden Paar
einander benachbarter Landmarks in dem zweiten Bild (105, 220) und
das Verwenden der Abstandstransformation bei der Darstellung des
ersten und des zweiten Bildes zur glatten Navigation (1024) zwischen
den einander benachbarten Landmarks derart, dass bei der Navigation
einander entsprechende Landmarks auf dem ersten Bild (103, 210)
und auf dem zweiten Bild (105, 220) etwa gleichzeitig erreicht werden.

Verfahren und Vorrichtung zur Synchronisation einander entsprechender Landmarks auf einer Anzahl Bilder

Alzheimer’s disease (AD) is caused by pathological changes including cortical thinning occurring throughout the brain.
Traditional methods for assessing cortical thickness are challenged by the sub-millimeter accuracy required for clinical
conditions and the convoluted nature of brain surface. Furthermore, there is a significant overlap of gray and white
matter intensities. A novel Brain Surface Intensity Model (BSIM) has been developed for use as a potential imaging
biomarker for neurodegenerative diseases. BSIM technique extracts MR intensity profiles perpendicular to a mathematically defined gray matter iso-intensity layer (GMIIL) at predefined reference points, fits that profile to BSIM,
and computes cortical thickness. A 3D visualization tool has been developed to evaluate intensity extraction and model
calculation. 29 normal subjects aged between 70 to 80 years from ADNI database were used to generate normal
references and measure individual Z-score cortical thinning. 30 age-matched AD subjects were used to study thinning
patterns. Significant cortical thinning (p < 0.0001) was found for AD group. 95% confidence interval of the cortical
thinning in AD patients was from 0.17 to 0.23 mm. The cortical thinning of the AD patients showed distinct features that differentiate AD patients from normal controls. The thickness measurements of 29 normal controls were validated by comparing with results from literature (p = 0.94). BSIM technique avoids complicated 3D segmentation of brain gray
and white matters, and simplifies the thickness calculation. Moreover, it is less affected by the image noise, inhomogeneity, partial volume effects, and the intensity overlap of the white and gray matters.

Quantitative measurement of MR cortical atrophy: MR brain surface intensity model (BSIM) and group and individual cortical thinning studies

PROBLEM TO BE SOLVED: To improve computer-supported detection or diagnosis, by using a computer-supported processing technique. SOLUTION: An image gathering method in an imaging system 10 includes a stage of making access to at least first image data from a first imaging system, a stage of processing the first image data by a CAD algorithm 104, a stage of gathering at least second image data on the basis of a result of the CAD algorithm, and a stage of confirming a result of CAD algorithm on the first image data by processing the second image data by the CAD algorithm. COPYRIGHT: (C)2005,JPO&NCIPI

Method and system for computer-supported target

The device (10) has an input for receiving the imaging data of an object from an imaging device (12). A user interface (20) for receiving a user input of an expected clinical condition of the object, which undergoes an examination in an imaging device. A processor which is connected to the input and the user interface to process the imaging data in combination with a knowledge base using an optimal image processing algorithm, in order to improve the visualization of an expected clinical condition in an image. An independent claim is also included for a method for the improvement of the visualization of clinical conditions.

Device for improvement of visualization of clinical conditions, has processor which is connected to input and user interface to process imaging data in combination with knowledge base using optimal image processing algorithm

Saad Ahmed Sirohey

Papers

Method and system for creating and using impact atlas

Verfahren und Vorrichtung zur Synchronisation einander entsprechender Landmarks auf einer Anzahl Bilder

Quantitative measurement of MR cortical atrophy: MR brain surface intensity model (BSIM) and group and individual cortical thinning studies

Method and system for computer-supported target

Device for improvement of visualization of clinical conditions, has processor which is connected to input and user interface to process imaging data in combination with knowledge base using optimal image processing algorithm