Handbook of Face Recognition
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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.
Is known to be more robust against internal and external facial variations (e.g., pose, lighting, expression, etc.) than simple PCA based matchers. They argued that the performance gain using FaceVACS is more realistic than that of a PCA matcher reported in other studies. Further, unlike other studies, they have used the entire FG-NET and MORPH-Album1 in the experiments. Another unique attribute of their studies is that the model is built on FG-NET and then evaluated on independent databases.
Multispectral imaging. 16.2.1 Multispectral Imaging Using Rotating Wheels In recent years, modern spectral image capture systems tend to rely on combinations of CCD cameras with various types of narrow or broad band filters. The images are then processed using common high-capacity computers with software developed to properly treat the spectral data. Therefore, capturing multispectral images can be accomplished by swapping narrow band-pass glass filters in front of the camera lens. It is common.
Face recognition (less than 1 m) for cooperative applications (e.g., access control) is the least difficult problem, whereas far field noncooperative applications (e.g., watchlist identification) in surveillance video is the most challenging. Applications in-between the above two categories can also be foreseen. For example, in face-based access control at a distance, the user is willing to be cooperative but he is unable to present the face in a favorable condition with respect to the camera.
Local deformations. The steps of the algorithm are as follows: The template shape is created in a bootstrapping process, starting with a manually created head model with an optimized mesh using discrete conformal mappings . It is first registered to the target scans, then the average over all registrations is used as a new template. This process is iterated a few times involving further manual corrections to achieve a good template shape. The template defines the parametrization of the.
Divided the probe images into eight bins of different FS and computed the percentage of correct rank 1 identification for each of these bins. There is a strong correlation between the FS and identification performance, indicating that the FS is a good measure of identification confidence. Fig. 6.8Identification results as a function of the fitting score 6.5.4 Virtual Views as an Aid to Standard Face Recognition Algorithms The face recognition vendor test (FRVT) 2002  was an independently.