By Wenyi Zhao, Rama Chellappa
Significant strides were made in face processing within the final ten years as a result of speedy starting to be want for protection in quite a few destinations world wide. A human eye can figure the main points of a particular face with relative ease. it really is this point of aspect that researchers are striving to create with ever evolving desktop applied sciences that would turn into our excellent mechanical eyes. the trouble that confronts researchers stems from turning a 3D item right into a second photo. That topic is roofed intensive from a number of assorted views during this volume.
This ebook starts off with a entire introductory bankruptcy should you are new to the sphere. A compendium of articles follows that's divided into 3 sections. the 1st covers easy elements of face processing from human to desktop. the second one offers with face modeling from computational and physiological issues of view. The 3rd tackles the complicated equipment, which come with illumination, pose, expression, and extra. Editors Zhao and Chellappa have compiled a concise and precious textual content for commercial examine scientists, scholars, and execs operating within the region of photo and sign processing.
*Contributions from over 35 prime specialists in face detection, reputation and picture processing
*Over a hundred and fifty informative photographs with sixteen photos in complete colour illustrate and supply perception into the main updated complex face processing equipment and techniques
*Extensive element makes this a need-to-own e-book for all concerned with photo and sign processing
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Extra resources for Face Processing: Advanced Modeling and Methods
During the past ten years, research on human action/behavior recognition from video has been very active and fruitful. Generic description of human behavior not particular to an individual is an interesting and useful concept. One of the main reasons for the feasibility of generic descriptions of human behavior is that the intraclass variations of human bodies, and in particular faces, is much smaller than the difference between the objects inside and outside the class. For the same reason, recognition of individuals within the class is difﬁcult.
The authors assume a planar surface patch at each feature point (landmark), and learn the transformations of “jets” under face rotation. Their results demonstrate substantial improvements in face recognition under rotation. Their method is also fully automatic, including face localization, landmark detection, and ﬂexible graph matching. The drawback of this method is its requirement for accurate landmark localization, which is not an easy task, especially when illumination variations are present.
Instead, the authors argue that the difference between two images of the same object is smaller than the difference between images of different objects. Based on such observations, the complexity of the ratio of two aligned images was proposed as the similarity measure. The authors noticed the similarity between this measure and the measure of simply comparing the edges. It is also clear that the proposed measure is not strictly illumination-invariant, because the measure changes for a pair of images of the same object when the illumination changes.