Advanced Correlation Filters for Face Recognition Using Low-Resolution Visual and Thermal Imagery

This paper presents the evaluation of face recognition performance using visual and thermal infrared (IR) face images with advanced correlation filter methods. Correlation filters are an attractive tool for face recognition due to features such as shift invariance, distortion tolerance, and graceful degradation. In this paper, we show that correlation filters perform very well when the face images are of significantly low resolution. Performing robust face recognition using low resolution images has many applications including human identification at a distance (HID). Minimum average correlation energy (MACE) filters and optimal trade-off synthetic discriminant function (OTSDF) filters are used in our experiments showing better performance over commercial face recognition algorithms such as FaceIt® based on Local Feature Analysis (LFA) using low resolution images. We also address the problems faced when using thermal images that contain eyeglasses which block the information around the eyes. Therefore we describe in detail a fully automated way of eyeglass detection and removal in thermal images resulting in a significant increase in thermal face recognition performance.