TAILIEUCHUNG - The Essential Guide to Image Processing- P24

The Essential Guide to Image Processing- P24:We are in the middle of an exciting period of time in the field of image processing. Indeed, scarcely a week passes where we do not hear an announcement of some new technological breakthrough in the areas of digital computation and telecommunication. | a b c FIGURE The average recognition rates across illumination the top row and across poses the bottom row for three cases. Case a shows the average recognition rate averaging over all illumination poses and all gallery sets obtained by the proposed algorithm using the top n matches. Case b shows the average recognition rate averaging over all illumination poses for the gallery set c27 f11 only obtained by the proposed algorithm using the top n matches. Case c shows the average recognition rate averaging over all illumination poses and all gallery sets obtained by the Eigenface algorithm using the top n matches. 700 CHAPTER 24 Unconstrained Face Recognition from a Single Image Face Modeling and Verification Across Age Progression 701 robust to aging effects. Researchers from psychophysics laid the foundations for studies related to facial aging effects. D arcy Thompson studied morphogenesis by means of geometric transformation functions applied on biological forms. Pittenger and Shaw 80 and Todd et al 81 identified certain forms of force configurations that when applied on 2D face profiles induce facial aging effects. Figure illustrates the effect of applying the revised cardioidal strain transformation model on profile faces. The aforementioned transformation model is said to reflect the remodeling of fluid filled spherical objects with applied pressure. O Toole et al 82 studied the effects of facial wrinkles in increasing the perceived age of faces. Ramanathan and Chellappa 59 developed a Bayesian agedifference classifier with the objective of developing systems that could perform face verification across age progression. The results from many such studies highlight the importance of developing computational models that characterize both growth-related shape variations and textural variations such as wrinkles and other skin artifacts in developing a facial aging model. In this section we shall present computational models that characterize shape .

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