I-Corps: Low False Negative 3-D Facial Recognition

Indiana University, Bloomington IN

Investigators

Abstract

The broader impact/commercial potential of this I-Corps project is the expansion of facial recognition technologies into the emissive infrared bands and the reduction of false negatives in the presence of large facial pose variation. Visible 2-D image-based machine learning techniques dominate the commercial facial recognition landscape while heterogeneous facial recognition techniques including 3-D to 2-D matching and infrared to visible matching have not seen similar widespread commercial deployment. The patented technology originates from an NSF Award and brings a crucial algorithmic component for commercial deployment that enhances heterogeneous facial recognition techniques while not relying on machine learning nor its concomitant large training database requirements. This in turn opens up the potential for improved automated surveillance against a given watch-list deployable in a host of public and private spaces and under a wider range of surveillance conditions than currently possible using 2-D commercial matching algorithms. This I-Corps project is a result of technology developed under an NSF Award which enables a radical improvement in 3-D facial recognition by reducing the computational costs for facial implicit surface generation and biometric capture originating from multispectral (visible and infrared) sources. The technology results in robust recognition in the presence of large facial pose variation and occlusion using 3-D facial implicit surfaces. The improvement over existing 2-D commercial facial recognition algorithms is evident under conditions typical of automated surveillance where large facial pose variation and poor lighting result in false negatives using conventional practice. The method and apparatus for 3-D facial recognition underlying this research, however, performs equally well under large facial pose variation and is able to seamlessly integrate multiple band infrared input along with visible image input for vastly lower false negative facial recognition even in the presence of low-light and nighttime conditions. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.