A novel biometric via hand structure using near-field microwave imaging

In this paper, we propose a novel biometric based on utilizing microwave images constructed from measuring a voltage that is related to the properties of the reflected microwave signal. The proposed technique requires collecting scans of the users´ hands using an open-ended rectangular waveguide operating at a frequency of 9.6 GHz in the near-field region. The microwave scan reflects the dielectric properties of the hand affected by the thickness, shape and structure of the hand at every point in the scan. The proposed technique is safe as it uses low power microwave signals in the range of 10-20 dBm. An in-house microwave hand-image database is collected using a microwave scanning system. The database is comprised of 370 images collected from 37 users. Radon and DCT transformations are used to extract distinguishing features from microwave images. Classification is done with polynomial classifiers. In the context of the polynomial classifier, a variety of dimensionality reduction schemes are examined. Amongst these schemes, spectral regression is found to give the best results in terms of accuracy, dimensionality of feature space and classifier stability. A user identification rate of 94.7% was achieved with a feature vector size of 36 elements only.