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

By Yu-Jin Zhang

Video and picture research of the human face presents a wealth of knowledge concerning the person, , together with age, habit, overall healthiness and career. With learn constantly being carried out into a number of functions of this box, a accomplished and specific quantity of the recent developments of face photo research is well-liked. Advances in face picture research: recommendations and applied sciences fulfills this want, reviewing and surveying new forward-thinking study and improvement in face picture research applied sciences. With greater than 30 best specialists from all over the world delivering complete insurance of assorted branches of face photo research, this publication is a necessary asset for college kids, researchers and practitioners engaged within the examine, study and improvement of face photograph research concepts.

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2. 3. 4. 5. Relevant: A reader of the result believes the benchmark reflects something important. Repeatable: There is confidence that the benchmark can be run a second time with the same result. Fair: All systems and/or software being compared can participate equally. Verifiable: There is confidence that the documented result is real. Economical: The test sponsors can afford to run the benchmark. The first characteristic could be the most important one as without relevance, the benchmark would be worthless.

How far can you get with a modern face recognition test set using only simple features? CVPR, 2591-2598. , et al. (1999). Fisher discriminant analysis with kernels. Proc. IWNNSP IX, 41-48. Price, J. , & Gee, T. F. (2005). Face recognition using direct, weighted linear discriminant analysis and modular subspaces. PR, 38(2), 209-219. Moghaddam, B. (2002). Principal manifolds and probabilistic subspaces for visual recognition. IEEE Trans. PAMI, 24(6), 780–788. , & Lumbreras, F. (2001). Topological principal component analysis for face encoding and recognition.

SMC-B, 36, 863–872. KEY TERMS AND DEFINITIONS Image Engineering (IE): is a general terms for all image techniques, abroad subject encompassing mathematics, physics, biology, physiology, psychology, electrical engineering, computer science, automation, etc. Its advances are also closely related to the development of telecommunications, biomedical engineering, remote sensing, document processing, industrial applications, etc. Face, Image, and Analysis Image Processing (IP): includes primarily the acquisition, representation, compression, enhancement, restoration, and reconstruction of images, etc.

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