A spoof detection method for contactless fingerprint collection utilizing spectrum and polarization diversity

The paper presents a spoof detection technique employing multi-spectral and multi-polarization imaging for a contactless fingerprint-capture system. While multispectral imaging has been proven to enable spoof detection for contact fingerprint imagers, these imagers typically rely on frustrated total internal reflection that requires a planar fingerprint, achieved by contact. The multispectral imaging method is based primarily on the difference in the spectral absorption profile between a real finger and a fake one. This paper will describe the expansion of this capability using blue and red light with contactless imaging in conjunction with polarization. This new method uses images at various rotated linear polarizations (each image representing a different value of specular and diffuse components), which are used to create the feature vectors representing the spectral and polarization diversity. The software extracts complex wavelet transforms (CWT) and FFT features from the images and builds a supervised learning method to train Support Vector Machine (SVM) classifiers. Experimental data was collected from a diversity of human fingers and silicon based phantoms molded from the corresponding humans. Fake and actual fingerprints were collected using individuals with a large diversity in skin tone, age, and finger dimensions. Our initial results, with an accuracy rate of at least 83%, are promising and imply that using the polarization diversity can enhance the spoof detection performance.