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

The vast majority of face recognition research has focused on the use of two-dimensional intensity images, and is covered in existing survey papers. This survey focuses on face recognition performed by matching two three-dimensional shape models, either alone or in combination with two-dimensional intensity images. Challenges involved in developing more accurate three-dimensional face recognition are identied. These include the need for improved sensors, recognition algorithms, and experimental methodology.

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A Survey Of 3D and Multi-Modal 3D+2D Face Recognition

This paper presents a method of evaluating unsupervised texture segmentation algorithms. The control scheme of texture segmentation has been conceptualized as two modular processes: (1) feature computation and (2) segmentation of homogeneous regions based on the feature values. Three feature extraction methods are considered: gray level co-occurrence matrix, Laws' texture energy and Gabor multi-channel filtering. Three segmentation algorithms are considered: fuzzy c-means clustering, square-error clustering and split-and-merge. A set of 35 real scene images with manually-specified ground truth was compiled. Performance is measured against ground truth on real images using region-based and pixel-based performance metrics.

Evaluation of texture segmentation algorithms

We present results of the first study to examine individual and multi-modal face recognition using 2D, 3D and infrared images of the same set of subjects. Each sensor captures different aspects of human facial features; appearance in intensity representing surface reflectance from a light source, shape data representing depth values from the camera, and the pattern of heat emitted, respectively. We employ a database containing a gallery set of 127 images and an accumulated time-lapse probe set of 297 images. Using a PCA-based approach tuned separately for 2 D, 3D and IR, we find rank-one recognition rates of 90.6% for 2D, 91.9% for 3D and 71.0% for IR. Combining each pair of modalities, we find a multi-modal rank-one recognition rate of 98.7% for 2D-3D, 96.6% for 2D-IR and 98.0% for 3D-IR. When all three modalities are combined, we obtain 100% recognition. The results shown in this study appear to support the conclusion that the path to higher accuracy and robustness in biometrics involves use of multiple biometrics rather than the best possible sensor and algorithm for a single biometric.

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Multi-biometrics using facial 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 "multimodal" approach) to the performance improvement obtained by using multiple intensity images (a "multisample" approach). Combining results from different types of imagery gives significantly higher recognition rates than are obtained by using a single intensity image. However, significantly higher recognition rates are also obtained by combining results from multiple intensity images. Overall, initial results indicate that, using an "eigen-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?

Multiple modality biometric approaches are proposed integrating two-dimensional face appearance with ear appearance, three-dimensional face shape, and the pattern of heat emission on face A single source biometric recognition method, such as face, has been shown to improve its identification rate by incorporating other biometric sources The investigation of multi-modal biometrics involves a variety of sensors For the recognition task, each sensor captures different aspects of human facial features; for example, appearance representing the levels of brightness on surface reflectance by a light source, shape data representing depth values defined at points on an object, and the pattern of heat emitted from an object The results of our multiple biometric approach shown in this investigation appear to support the conclusion that the path to higher accuracy and robustness in biometrics involves the use of multiple biometrics rather than the best possible sensor and algorithm for a single biometric A new evaluation scheme is designed to assess the improvement gained by multiple biometrics Because multi-modal recognition employs multiple samples of facial data, it is also possible that the improvement achieved over considering multiple samples from all modalities for recognition Therefore, this evaluation scheme will determine the recognition accuracy gained by multiple modality approach and multiple sample approach 
Also, a new algorithm for 3D face recognition is proposed for handling expression variation It uses a surface registration-based technique for 3D face recognition We evaluate and compare the performance of approaches to 3D face recognition based on PCA-based and on iterative closest point algorithms The proposed 3D face recognition method is fully automatic to use to initialize the 3D matching The evaluation results show that the proposed algorithm substantially improves performance in the case of varying facial expression 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 The proposed method outperforms 3D eigenfaces when 3D face scans were acquired in different times without expression changes and also with expression changes

K.I. Chang

Papers

A Survey Of 3D and Multi-Modal 3D+2D Face Recognition

Evaluation of texture segmentation algorithms

Multi-biometrics using facial appearance, shape and temperature

Face Recognition Using 2-D, 3-D, and Infrared: Is Multimodal Better Than Multisample?

New multi-biometric approaches for improved person identification