By Yu-Jin Zhang
Video and picture research of the human face presents a wealth of knowledge concerning the person, , together with age, habit, healthiness and occupation. With learn always being carried out into a number of purposes of this box, a entire and specified quantity of the hot developments of face picture research is favourite. Advances in face photograph research: options and applied sciences fulfills this want, reviewing and surveying new forward-thinking study and improvement in face snapshot research applied sciences. With greater than 30 prime specialists from worldwide offering finished insurance of varied branches of face snapshot research, this e-book is a priceless asset for college kids, researchers and practitioners engaged within the research, examine and improvement of face snapshot research options.
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Extra info for Advances in Face Image Analysis: Techniques and Technologies
D. (2009). 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.
Li, S. , & Jain, A. K. (2004). Handbook of Face Recognition. New York: Springer. 12 Face, Image, and Analysis Liu, C. , & Wechsler, H. (2000). Robust coding schemes for indexing and retrieval from large face databases. IEEE Trans. IP, 9(1), 132–137. , & Ray, R. (1995). Comparison of neural network algorithms for face recognition. Simulation, 65(1), 37–51. 1177/003754979506500105 Phillips, P. , et al. (2002). Face recognition vendor test 2002: Evaluation report. NIST, TR-IR 6965. , DiCarlo, J. , & Cox, D.
2001). The face shape and the albedo are extracted by few subject images, by means of shape from shading. The 3D model is then used to synthesize a wide set of face views in different pose and illumination. The main underlying hypothesis is that, for each fixed pose of the object/face, all its views under different illuminations form a convex cone in the image space. Such convex cone is therefore computed for every subject and pose, and then approximated by means of a low-dimensional linear subspace.