By Yu-Jin Zhang
Video and photograph research of the human face offers a wealth of knowledge in regards to the person, together with age, habit, healthiness and occupation. With learn regularly being performed into a number of purposes of this box, a complete and distinctive quantity of the recent developments of face picture research is in demand.
Advances in Face photograph research: thoughts and Technologies fulfills this want, reviewing and surveying new forward-thinking learn and improvement in face picture research applied sciences. With greater than 30 top specialists from world wide offering finished insurance of assorted branches of face snapshot research, this publication is a useful asset for college kids, researchers and practitioners engaged within the examine, examine and improvement of face photo research techniques.
Read Online or Download Advances in Face Image Analysis: Techniques and Technologies PDF
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Extra resources for Advances in Face Image Analysis: Techniques and Technologies
Mirza, S. M. (2004). Face recognition: a review and comparison of HMM, PCA, ICA and neural networks. E-Tech, 41-46. , & Li, B. X. (2009). A Compressive Sensing Approach for Expression-Invariant Face Recognition. CVPR. , & Feng, D. (2008). Building highly realistic facial modeling and animation: A survey. The Visual Computer, 24(1), 13–30. , & Prasad, M. (2003). Face recognition in hyperspectral images. IEEE Trans. PAMI, 25, 1552–1560. Park, C. , & Park, H. (2008). A comparison of generalized linear discriminant analysis algorithms.
LDA (Linear Discriminant Analysis) (Lu, 2003) has been proposed as a better alternative to PCA, since it expressly provides discrimination among the classes. e. to maximize the betweenclass differences and minimize the within-class ones. It is worth noticing that LDA provides better classification performances than PCA only when a wide training set is available, as discussed by Martinèz (Martinèz, 2009). Further studies also strengthen such argument by expressly tackling the classical SSS (Small Sample Size) problem.
2002). Face recognition using kernel principal component analysis. IEEE Signal Processing Letters, 9(2), 40–42. , & Sirovich, L. (1990). Application of the Karhunen-Loeve procedure for the characterization of human faces. IEEE Trans. PAMI, 12, 103–108. Kramer, M. A. (1991). Nonlinear principal components analysis using auto-associative neural networks. AIChE Journal. American Institute of Chemical Engineers, 32(2), 233–243. , Zhang, J. , & Xie, L. (2008). Developments and applications of nonlinear principal component analysis - A review.