Face Recognition in Low-Light Environments Using Fusion of Thermal Infrared and Intensified Imagery

摘要

This paper presents a study of face recognition performance as a function of light level using intensified near infrared imagery in conjunction with thermal infrared imagery. Intensification technology is the most prevalent in both civilian and military night vision equipment, and provides enough enhancement for human operators to perform standard tasks under extremely low-light conditions. We describe a comprehensive data collection effort undertaken by the authors to image subjects under carefully controlled illumination and quantify the performance of standard face recognition algorithms on visible, intensified and thermal imagery as a function of light level. Performance comparisons for automatic face recognition are reported using the standardized implementations from the CSU Face Identification Evaluation System, as well as Equinox own algorithms. The results contained in this paper should constitute the initial step for analysis and deployment efface recognition systems designed to work in low-light level conditions.