Anti-Spoof Reliable Biometry of Fingerprints Using En-Face Optical Coherence Tomography

Optical coherence tomography (OCT) is a relatively new imaging technology which can produce high-reso- lution images of three-dimensional structures. OCT has been mainly used for medical applications such as for ophthalmology and dermatology. In this study we demonstrate its capability in providing much more re- liable biometry identification of fingerprints than conventional methods. We prove that OCT can serve se- cure control of genuine fingerprints as it can detect if extra layers are placed above the finger. This can pre- vent with a high probability, intruders to a secure area trying to foul standard systems based on imaging the finger surface. En-Face OCT method is employed and recommended for its capability of providing not only the axial succession of layers in depth, but the en-face image that allows the traditional pattern identification. Another reason for using such OCT technology is that it is compatible with dynamic focus and therefore can provide enhanced transversal resolution and sensitivity. Two En-Face OCT systems are used to evaluate the need for high resolution and conclusions are drawn in terms of the most potential commercial route to ex- ploitation

RF Localization in Indoor Environment

In this paper indoor localization system based on the RF power measurements of the Received Signal Strength (RSS) in WLAN environment is presented. Today, the most viable solution for localization is the RSS fingerprinting based approach, where in order to establish a relationship between RSS values and location, different machine learning approaches are used. The advantage of this approach based on WLAN technology is that it does not need new infrastructure (it reuses already and widely deployed equipment), and the RSS measurement is part of the normal operating mode of wireless equipment. We derive the Cramer-Rao Lower Bound (CRLB) of localization accuracy for RSS measurements. In analysis of the bound we give insight in localization performance and deployment issues of a localization system, which could help designing an efficient localization system. To compare different machine learning approaches we developed a localization system based on an artificial neural network, k-nearest neighbors, probabilistic method based on the Gaussian kernel and the histogram method. We tested the developed system in real world WLAN indoor environment, where realistic RSS measurements were collected. Experimental comparison of the results has been investigated and average location estimation error of around 2 meters was obtained