Fusion-Based Approach for Long-Range Night-Time Facial Recognition

摘要

Long range identification using facial recognition is being pursued as a valuable surveillance tool. The capability to perform this task covertly and in total darkness greatly enhances the operators' ability to maintain a large distance between themselves and a possible hostile target. An active-SWIR video imaging system has been developed to produce high-quality long-range night/day facial imagery for this purpose. Most facial recognition techniques match a single input probe image against a gallery of possible match candidates. When resolution, wavelength, and uncontrolled conditions reduce the accuracy of single-image matching, multiple probe images of the same subject can be matched to the watch-list and the results fused to increase accuracy. If multiple probe images are acquired from video over a short period of time, the high correlation between the images tends to produce similar matching results, which should reduce the benefit of the fusion. In contrast, fusing matching results from multiple images acquired over a longer period of time, where the images show more variability, should produce a more accurate result. In general, image variables could include pose angle, field-of-view, lighting condition, facial expression, target to sensor distance, contrast, and image background. Long-range short wave infrared (SWIR) video was used to generate probe image datasets containing different levels of variability. Face matching results for each image in each dataset were fused, and the results compared.