Recognition Performance of Cross-Spectral Periocular Biometrics and Partial Face at Short and Long Standoff Distance

Abstract

Matching partial heterogeneous face images to a gallery of visible images is a challenging research problem. This scenario is motivated by a number of surveillance applications such as recognition of subjects at night or in the presence of challenging environmental conditions. Standoff distances may range from a meter to hundred meters. Our latest experiments have shown that a face recognition software recently developed in our research group can be adapted to perform cross spectral matching of partial face images. The images are encoded with Gabor Generalized Local Binary Patterns and Gabor Weber operators and matched by means of a Kullbuck-Leibler metric. Our analysis has shown that three separate face regions such as (1) eyes and nasal bridge, (2) cheeks and nasal tip, and (3) mouth and a part of the chin display similar matching performance. Furthermore, we have evaluated performance of periocular regions. For a short standoff distance of 1.5 meters and a database of 48 classes, matching a Short Wave Infrared (SWIR) periocular region against visible regions resulted in 0.7 Genuine Accept Rate (GAR) at False Accept Rate (FAR) set to 0.01. For a long standoff distance of 106 meters and a database of 48 classes, matching SWIR against visible periocular regions yielded 0.4 GAR at FAR equal to 0.1. For a short standoff distance of 1.5 meters and a database of 200 classes, matching a Medium Wave Infrared (MWIR) periocular region against visible regions resulted in 0.35 GAR at FAR set to 0.1.