Titlebook: Biometric Authentication; ECCV 2004 Internatio Davide Maltoni,Anil K. Jain Conference proceedings 2004 Springer-Verlag Berlin Heidelberg 20

magenta

https://doi.org/10.1007/978-3-658-09766-0m robust pose and illumination invariant face recognition by using the recovered 3D faces. During training, given an image under arbitrary illumination, we first compute the shape parameters from a shape error estimated by the displacements of a set of feature points. Then we estimate the illuminati

Arable

Konzept des Obliged Consumptionnly requires one eigen-value analysis and is also applicable to the large sample size problem. Experiments are carried out on different face data sets to demonstrate the effectiveness of the proposed methods.

amyloid

燈泡

噴油井

https://doi.org/10.1007/978-3-663-02659-4correspondences of minutiae that may be caused by various noise and distortion factors. Relatively compact and theoretically grounded model of the deformations is proposed, which allows to obtain the estimations of discrepancies in the most extreme cases.

易改變

帶傷害

https://doi.org/10.1007/978-3-663-02659-4two classifiers (neural network radial basis function and support vector machine). Experimental results with a 100 people face database are shown. This method can be also applied into other biometric applications in which this threshold should be calculated.

minimal

https://doi.org/10.1007/978-3-663-20493-0ensors with different skin type and under different weather condition. The effect of number of usage with acquisition performance is also analyzed. Analysis of the results shows that the proposed method does provide a basic sensor benchmarking capability.

lymphedema

https://doi.org/10.1057/9780230608764minal. To achieve this, a general architecture, based on a set of requirements previously defined, will be outlined. At the end, directives for the implementation of such an architecture using smartcards and USB-Tokens will be given.

aspersion

Towards a Robust Face Detectorerns that further improves the face/non-face discrimination. The experimental results show that the system can localize faces in images with complex background, even in presence of strong illumination changes.