Fractional-Step Dimensionality Reduction
IEEE Transactions on Pattern Analysis and Machine Intelligence
History, Current Status, and Future of Infrared Identification
CVBVS '00 Proceedings of the IEEE Workshop on Computer Vision Beyond the Visible Spectrum: Methods and Applications (CVBVS 2000)
Comparison of visible and infra-red imagery for face recognition
FG '96 Proceedings of the 2nd International Conference on Automatic Face and Gesture Recognition (FG '96)
Face Recognition Vendor Test 2002
AMFG '03 Proceedings of the IEEE International Workshop on Analysis and Modeling of Faces and Gestures
Face recognition with visible and thermal infrared imagery
Computer Vision and Image Understanding - Special issue on Face recognition
Face recognition: A literature survey
ACM Computing Surveys (CSUR)
Thermal Face Recognition Over Time
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 4 - Volume 04
Recent advances in visual and infrared face recognition: a review
Computer Vision and Image Understanding
IR and visible light face recognition
Computer Vision and Image Understanding
A robust method for detecting facial orientation in infrared images
Pattern Recognition
Thermal face recognition in an operational scenario
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Face recognition with radial basis function (RBF) neural networks
IEEE Transactions on Neural Networks
Infrared face recognition based on histogram and k-nearest neighbor classification
ISNN'10 Proceedings of the 7th international conference on Advances in Neural Networks - Volume Part II
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It has been found that facial thermograms vary with ambient temperature, as well as other internal and external conditions, and result in severe decline in the facial recognition rate. To tackle this problem, a skin heat transfer (SHT) model based on thermal physiology is derived in this paper. The proposed model converts the facial thermograms into blood-perfusion data, which is revealed to reduce the within-class scatter of face images. The advantage of the derived blood-perfusion data over the raw thermograms for recognition is analyzed by the normalized reverse cumulative histogram. It is shown that blood-perfusion data are more consistent in representing facial features. The experiments conducted on both same-session and time-lapse data have further demonstrated that (1) the blood-perfusion data are less sensitive to ambient temperature, physiological and psychological conditions if the human bodies are in the steady state; (2) for time-lapse data, the performance with the blood-perfusion data is nearly identical to that of the same-session data, while the recognition rate with the temperature data dramatically decreases in this case. The major contributions of this work are the well-grounded infrared data preprocessing and the corresponding face recognition system.