Comparison of view-based face recognition algorithms on visible and infra-red imagery

This paper presents initial results in a study comparing the effectiveness of visible and infra-red (IR) imagery for detecting and recognizing faces in areas where personnel identification is critical, (e.g., airports and secure buildings). We compare the effectiveness of visible versus IR imagery by running three face recognition algorithms on a database of images collected for this study. There are both IR and visible images for each person in the database collected using the same scenarios. We used three very different feature-extraction and decision-making algorithms for our study to insure that the comparisons would not depend on a particular processing technique. We also present recognition results when visible and infra-red decision metrics are fused. The recognition results show that both visible and IR imagery perform similarly across algorithms and that fusion of IR and visible imagery is a viable means of enhancing performance beyond that of either acting alone. We examine the relative importance of different regions of the face for recognition. We also discuss practical issues of implementation, along with plans for the next phase of the study, face detection in an uncontrolled environment. Preliminary face detection experiments are described.