Face Processing: Advanced Modeling and Methods: Advanced Modeling and Methods (Google eBook)
Wenyi Zhao, Rama Chellappa
Academic Press, Jul 28, 2011 - Computers - 768 pages
Major strides have been made in face processing in the last ten years due to the fast growing need for security in various locations around the globe. A human eye can discern the details of a specific face with relative ease. It is this level of detail that researchers are striving to create with ever evolving computer technologies that will become our perfect mechanical eyes. The difficulty that confronts researchers stems from turning a 3D object into a 2D image. That subject is covered in depth from several different perspectives in this volume.
This book begins with a comprehensive introductory chapter for those who are new to the field. A compendium of articles follows that is divided into three sections. The first covers basic aspects of face processing from human to computer. The second deals with face modeling from computational and physiological points of view. The third tackles the advanced methods, which include illumination, pose, expression, and more. Editors Zhao and Chellappa have compiled a concise and necessary text for industrial research scientists, students, and professionals working in the area of image and signal processing.
*Contributions from over 35 leading experts in face detection, recognition and image processing
*Over 150 informative images with 16 images in FULL COLOR illustrate and offer insight into the most up-to-date advanced face processing methods and techniques
*Extensive detail makes this a need-to-own book for all involved with image and signal processing
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3D face model 3D model albedo algorithm Analysis and Machine applications approach basis images biometric BRDF camera Chellappa classifier coefficients combination components Computer Vision Conference on Computer coordinates cortex database distance distribution eigenfaces eigenvectors Equation error estimate example face detection face images face perception face-recognition systems facial expression facial features FERET frame frontal function geometry Gesture Recognition human faces identification identity IEEE illumination input Lambertian lighting conditions linear linear subspace LWIR Machine Intelligence matching matrix method modalities morphable model motion multimodal neural object obtained optimization parameters Pattern Analysis Pattern Recognition pixels points probe image problem Proc recognition performance recognition rate recognize reconstruction representation robust rotation sample shown in Figure SIGGRAPH space spherical harmonics statistical stereo subset subspace surface normals techniques texture texture map tracking training images values variations vector video sequence Vision and Pattern visual
Page 2 - Although extremely reliable methods of biometric personal identification exist, eg, fingerprint analysis and retinal or iris scans, these methods rely on the cooperation of the participants, whereas a personal identification system based on analysis of frontal or profile images of the face is often effective without the participant's co-operation or knowledge.