Advances in Face Image Analysis: Techniques and Technologies - download pdf or read online

By Yu-Jin Zhang

ISBN-10: 1615209913

ISBN-13: 9781615209910

ISBN-10: 1615209921

ISBN-13: 9781615209927

Video and snapshot research of the human face presents a wealth of data concerning the person, , together with age, habit, overall healthiness and career. With examine consistently being carried out into a number of functions of this box, a accomplished and specified quantity of the recent developments of face snapshot research is sought after. Advances in face picture research: thoughts and applied sciences fulfills this desire, reviewing and surveying new forward-thinking learn and improvement in face photograph research applied sciences. With greater than 30 best specialists from worldwide delivering entire assurance of varied branches of face picture research, this e-book is a invaluable asset for college kids, researchers and practitioners engaged within the learn, learn and improvement of face snapshot research options.

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Additional resources for Advances in Face Image Analysis: Techniques and Technologies

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Wiskott et al. (1997) go further using DLA and develop an Elastic Bunch Graph Matching (EBGM) method based on Gabor wavelets, to label and recognize human faces. In general, DLA is superior to other face recognition techniques, in terms of rotation invariance; however, the matching process is computationally expensive. F. -L. Dugelay (2003) propose a novel probabilistic deformable model for face mapping, with a similar philosophy to EBGM. It is based on a bi-dimensional extension of the 1D-HMM (Hidden Markov Model).

F. -L. Dugelay (2003) propose a novel probabilistic deformable model for face mapping, with a similar philosophy to EBGM. It is based on a bi-dimensional extension of the 1D-HMM (Hidden Markov Model). There are two main differences between this method and the original EBGM. First, all the parameters of M are automatically trained, so accounting for the elastic properties of the different parts of the face. Secondly, the model M is shared among all faces, so that little enrolment data is necessary.

2005). Recent advances in visual and infrared face recognition -- A review. Computer Vision and Image Understanding, 97(1), 103–135. 1016/j. 001 Erik, M. , & Trivedi, M. M. (2009). Head pose estimation in computer vision: A survey. IEEE Trans. PAMI, 31(4), 607–626. , Liu, Y. , & Perona, P. (2007). On constructing facial similarity maps. Proc. CVPR, 1-8. Hong, Z. (1991). Algebraic feature extraction of image for recognition. PR, 24, 211-219. , & Park, H. (2004). Generalizing discriminant analysis using the generalized singular value decomposition.

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Advances in Face Image Analysis: Techniques and Technologies by Yu-Jin Zhang


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