Moisture-insensitive optical fingerprint scanner based on polarization resolved in-finger scattered light

Abstract

A moisture-insensitive optical fingerprint scanner (FPS) that is based on polarization resolved in-finger light is proposed and realized. Incident visible light, which is selectively fed to a fingerprint sample via a polarization beam splitter (PBS), is deemed to be partially scattered backward by tissues associated with the skin of the finger. The backscattered light is mostly index-guided in the ridge comprising the fingerprint, which has a higher refractive index, and is drastically dispersed in the valley, which is typically filled with water or air and so has a lower index. However, when light reflects directly off the surface of the finger skin, it fundamentally prevents the scanned image from being determined. The proposed FPS produces bright and dark intensity patterns that are alternately created on the surface of the PBS and correspond to the ridges and valleys, respectively. Thus, this method can especially distinguish between a fake synthetic fingerprint and a genuine fingerprint due to its use of in-finger scattered light. The scanner has been rigorously designed by carrying out ray-optic simulations depending on the wavelength, with tissue-induced scattering taken into account. The device was constructed by incorporating a wire-grid type PBS in conjunction with visible LED sources, including blue, green and red. The scanner adopting a blue LED, which exhibits the strongest light scattering, resulted in the best fingerprint image, enabling enhanced fidelity under the wet and dry situations. Finally, a fake synthetic fingerprint could be successfully discriminated.

Simulating the Effects of Skin Thickness and Fingerprints to Highlight Problems With Non-Invasive RF Blood Glucose Sensing From Fingertips

“…They are formed of ridges and valleys with an average depth of 50 µm [25]. Although this appears to be a small feature to be neglected in non-invasive BGL measurements or simulations, the frequency shift caused by the fingerprints is greater than that caused by the actual glucose level variation.…”

“…Simulations were performed by varying the valley depth in each fingerprint with values of 5 µm, 10 µm, 20 µm, 40 µm and 80 µm, assuming a 50 µm average valley depth for an uncompressed fingerprint [25,26]. Mesh size was set to 20 cells at 5 GHz in MWS.…”

Photoacoustic tomography of fingerprint and underlying vasculature for improved biometric identification

“…Optical sensors capture the high-resolution optical contrasts between ridges and valleys of the fingerprint. Optical sensors are widely used in commercial and governmental settings 3 . Capacitive sensors measure the capacitance difference between ridges and valleys, and are the most common type of sensor used in smartphones 4 .…”

Effect of the Papaya Latex on the Visibility of Fingerprint

“…There are many different sensing methods to obtain the ridge and valley patterns of finger skin or fingerprint. Therefore, all the developed fingerprint sensors have been tried to improve the visibility of the ridges and the ridge-valley contrast in which the trend is toward compact and low-cost device (Back et al, 2016;Cui et al, 2016;Cheng and Larin, 2006;Nedjah et al, 2019;Ivanov and Baras, 2019).…”