Over the last couple of years, face recognition researchers have been developing new techniques. These developments are being fueled by advances in computer vision techniques, computer design, sensor design, and interest in fielding face recognition systems. Such advances hold the promise of reducing the error rate in face recognition systems by an order of magnitude over Face Recognition Vendor Test (FRVT) 2002 results. The face recognition grand challenge (FRGC) is designed to achieve this performance goal by presenting to researchers a six-experiment challenge problem along with data corpus of 50,000 images. The data consists of 3D scans and high resolution still imagery taken under controlled and uncontrolled conditions. This paper describes the challenge problem, data corpus, and presents baseline performance and preliminary results on natural statistics of facial imagery.

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Overview of the face recognition grand challenge

Purpose: The development of computer-aided diagnostic (CAD) methods for lung nodule detection, classification, and quantitative assessment can be facilitated through a well-characterized repository of computed tomography (CT) scans. The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI) completed such a database, establishing a publicly available reference for the medical imaging research community. Initiated by the National Cancer Institute (NCI), further advanced by the Foundation for the National Institutes of Health (FNIH), and accompanied by the Food and Drug Administration (FDA) through active participation, this public-private partnership demonstrates the success of a consortium founded on a consensus-based process. Methods: Seven academic centers and eight medical imaging companies collaborated to identify, address, and resolve challenging organizational, technical, and clinical issues to provide a solid foundation for a robust database. The LIDC/IDRI Database contains 1018 cases, each of which includes images from a clinical thoracic CT scan and an associated XML file that records the results of a two-phase image annotation process performed by four experienced thoracic radiologists. In the initial blinded-read phase, each radiologist independently reviewed each CT scan and marked lesions belonging to one of three categories (" noduleâ�¥3 mm," " nodule<3 mm," and "non- noduleâ�¥3 mm "). In the subsequent unblinded-read phase, each radiologist independently reviewed their own marks along with the anonymized marks of the three other radiologists to render a final opinion. The goal of this process was to identify as completely as possible all lung nodules in each CT scan without requiring forced consensus. Results: The Database contains 7371 lesions marked "nodule" by at least one radiologist. 2669 of these lesions were marked " nodulâ�¥3 mm " by at least one radiologist, of which 928 (34.7) received such marks from all four radiologists. These 2669 lesions include nodule outlines and subjective nodule characteristic ratings. Conclusions: The LIDC/IDRI Database is expected to provide an essential medical imaging research resource to spur CAD development, validation, and dissemination in clinical practice. Â© 2011 U.S. Government.

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The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI): a completed reference database of lung nodules on CT scans.

This highly anticipated new edition provides a comprehensive account of face recognition research and technology, spanning the full range of topics needed for designing operational face recognition systems. After a thorough introductory chapter, each of the following chapters focus on a specific topic, reviewing background information, up-to-date techniques, and recent results, as well as offering challenges and future directions. Features: fully updated, revised and expanded, covering the entire spectrum of concepts, methods, and algorithms for automated face detection and recognition systems; provides comprehensive coverage of face detection, tracking, alignment, feature extraction, and recognition technologies, and issues in evaluation, systems, security, and applications; contains numerous step-by-step algorithms; describes a broad range of applications; presents contributions from an international selection of experts; integrates numerous supporting graphs, tables, charts, and performance data.

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Handbook of Face Recognition

Generative 3D face models are a powerful tool in computer vision. They provide pose and illumination invariance by modeling the space of 3D faces and the imaging process. The power of these models comes at the cost of an expensive and tedious construction process, which has led the community to focus on more easily constructed but less powerful models. With this paper we publish a generative 3D shape and texture model, the Basel Face Model (BFM), and demonstrate its application to several face recognition task. We improve on previous models by offering higher shape and texture accuracy due to a better scanning device and less correspondence artifacts due to an improved registration algorithm. The same 3D face model can be fit to 2D or 3D images acquired under different situations and with different sensors using an analysis by synthesis method. The resulting model parameters separate pose, lighting, imaging and identity parameters, which facilitates invariant face recognition across sensors and data sets by comparing only the identity parameters. We hope that the availability of this registered face model will spur research in generative models. Together with the model we publish a set of detailed recognition and reconstruction results on standard databases to allow complete algorithm comparisons.

A 3D Face Model for Pose and Illumination Invariant Face Recognition

Skin color has proven to be a useful and robust cue for face detection, localization and tracking. Image content filtering, content-aware video compression and image color balancing applications can also benefit from automatic detection of skin in images. Numerous techniques for skin color modelling and recognition have been proposed during several past years. A few papers comparing different approaches have been published [Zarit et al. 1999], [Terrillon et al. 2000], [Brand and Mason 2000]. However, a comprehensive survey on the topic is still missing. We try to fill this vacuum by reviewing most widely used methods and techniques and collecting their numerical evaluation results.

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A Survey on Pixel-Based Skin Color Detection Techniques

This study considers face recognition using multiple imaging modalities. Face recognition is performed using a PCA-based algorithm on each of three individual modalities: normal 2D intensity images, range images representing 3D shape, and infra-red images representing the pattern of heat emission. The algorithm is separately tuned for each modality. For each modality, the gallery consists of one image of each of the same 127 persons, and the probe set consists of 297 images of these subjects, acquired with one or more week's time lapse. In this experiment, we find a rank-one recognition rate of 71% for infra-red, 91% for 2D, 92% for 3D. We also consider the multi-modal combination of each pair of modalities, and find a rank-one recognition rate of 97% for 2D plus infra-red, 98% for 3D plus infra-red, and 99% for 3D plus 2D. The combination of all three modalities yields a rank-one recognition rate of 100%. We conclude that multi-modal face recognition appears to offer great potential for improved accuracy over using a single 2D image. Larger and more challenging experiments are needed in order to explore this potential.© (2004) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Evaluation of multimodal biometrics using appearance, shape, and temperature

This work examines face recognition using normal intensity images, infrared images, three-dimensional shape, and combinations of these. We compare the performance improvement obtained by combining three-dimensional or infrared with normal intensity images (a Bmultimodal( ap- proach) to the performance improvement obtained by using multiple intensity images (a Bmultisample( approach). Com- bining results from different types of imagery gives signifi- cantly higher recognition rates than are obtained by using a single intensity image. However, significantly higher recogni- tion rates are also obtained by combining results from multiple intensity images. Overall, initial results indicate that, using an Beigen-face( recognition algorithm and weighted score fusion, multisample techniques can result in a performance increase comparable to that of multimodal techniques.

Face Recognition Using 2-D, 3-D, and Infrared: Is Multimodal Better Than Multisample? Use of multiple images of a face appears to improve recognition accuracy regardless of the type of images that are taken.

The use of dense range scans of the face to supplement or supplant visible-light images in face recognition systems has been a subject of recent interest in the biometrics community. This paper summarizes recent research activities designed to assess the potential of a high-resolution 3D face scan as a viable biometric, using a large database of such scans. We show that 3D faces offer potential for this application but also exhibit challenges that must be addressed before systems based on 3D face can be fielded. We also describe preliminary results from a study of 3D hand shape biometrics employing curvature classification of the finger skin surface.

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Three-dimensional face and finger biometrics

We present a new algorithm for 3D face recognition, and compare its performance to that of previous approaches. We focus especially on the case of facial expression change between gallery and probe images. We first establish performance comparisons using a PCA (“eigenface”) algorithm and an ICP (iterative closest point) algorithm similar to ones reported in the literature. Experimental results show that the performance of either approach degrades substantially in the case of varying facial expression. Then we introduce a new algorithm that independently matches multiple facial regions and creates a fused result. This algorithm is fully automated and used no manually selected landmark points. Experimental results show that our new algorithm substantially improves performance in the case of varying facial expression. Our experimental results are based on the largest 3D face dataset to date, with 449 persons, over 4,000 3D images, and substantial lapse between gallery and probe images. This is the first study to compare the PCA and ICP approaches to 3D face recognition, and the first to propose a multiple-region approach to coping with expression variation in 3D face recognition.

K.I. Chang

Papers

Evaluation of multimodal biometrics using appearance, shape, and temperature

Face Recognition Using 2-D, 3-D, and Infrared: Is Multimodal Better Than Multisample? Use of multiple images of a face appears to improve recognition accuracy regardless of the type of images that are taken.

Three-dimensional face and finger biometrics

A New Algorithm for Handling Expression Variation in 3D Face Recognition