NASK Biometria twarzy

NASK
Źródło: http://www.nask.pl/pl/dzialalnosc/badania-i-rozwoj/biometria/wybrane-publikacje/82,Biometria-twarzy.html
Wygenerowano: Środa, 8 marca 2017, 13:19
Biometria twarzy
Aplikacje mobilne, rozpoznawanie twarzy, przetwarzanie obrazów
W. Gutfeter. Face 3D biometrics goes mobile-serching for application. In: 2nd IEEE
International Conference on Cybernetics (CYBCONF 2015), Special Session on Reliable
Biometrics (BIORELIABILITY 2015). Gdynia, Polska; 2015:489-494.
Streszczenie
Praca przedstawia metodę przetwarzania obrazów umożliwiającą rozpoznawanie
twarzy przy pomocy kamer sensorów mobilnych. Proponowane są metody
przetwarzania danych pomiarowych pochodzących z urządzeń mobilnych
umożliwiające skuteczną identyfikację cech biometrycznych.
Abstract
This paper presents an acquisition procedure and method of processing spatial images
for face recognition with the use of a novel type of scanning device, namely mobile
depth sensor Structure. Depth sensors, often called RGBD cameras, are able to deliver
3D images with a frame rate 30-60 frames per second, however they have relatively
low resolution and a high level of noise. This kind of data is compared here with a high
quality scans enrolled by the structural light scanner, for which the acquisition time is
approximately 1.5 s for a single image, and which - because of its size - cannot be
classified as a portable device. The purpose of this work was to find the method that
will allow us to extract spatial features from mobile data sources analyzed here only in
a static context. We transform the 3D data into local surface features and then into
vectors of unified length by use of the Moving Least Squares method applied to a
predefined grid of points on a reference cylinder. The feature matrices were calculated
for various image features, and used in PCA analysis. Finally, the verification errors
were calculated and compared to those obtained for stationary devices. The results
show that single-image mobile sensor images lead to the results inferior to those of
stationary sensors. However, we suggest a dynamic depth stream processing as the
next step in the evolution of the described method. The presented results show that
by including multi-frame processing into our method, it is likely to gain the accuracy
similar to those obtained for a stationary device under controlled laboratory conditions.
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Biometria twarzy