Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition: The Problem of Compensating for Changes in Illumination Direction
IEEE Transactions on Pattern Analysis and Machine Intelligence
What Is the Set of Images of an Object Under All Possible Illumination Conditions?
International Journal of Computer Vision
Direct Least Square Fitting of Ellipses
IEEE Transactions on Pattern Analysis and Machine Intelligence
How Should We RepresentFaces for Automatic Recognition?
IEEE Transactions on Pattern Analysis and Machine Intelligence
Local feature analysis: a statistical theory for information representation and transmission
Local feature analysis: a statistical theory for information representation and transmission
Fusion of Visual and Thermal Signatures with Eyeglass Removal for Robust Face Recognition
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 8 - Volume 08
Journal of Cognitive Neuroscience
Face recognition by independent component analysis
IEEE Transactions on Neural Networks
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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.