IEEE Transactions on Pattern Analysis and Machine Intelligence
Face recognition by elastic bunch graph matching
Intelligent biometric techniques in fingerprint and face recognition
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume I - Volume I
Memory-Based Face Recognition for Visitor Identification
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
A Comparative Analysis of Face Recognition Performance with Visible and Thermal Infrared Imagery
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 4 - Volume 4
PCA-Based Face Recognition in Infrared Imagery: Baseline and Comparative Studies
AMFG '03 Proceedings of the IEEE International Workshop on Analysis and Modeling of Faces and Gestures
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
Face Recognition in the Thermal Infrared Spectrum
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 8 - Volume 08
Recent advances in visual and infrared face recognition: a review
Computer Vision and Image Understanding
Journal of Cognitive Neuroscience
Face recognition by independent component analysis
IEEE Transactions on Neural Networks
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Fusion architecture for efficient visual and thermal face recognition biometric system is presented in this paper. Both Data fusion and decision fusion are employed in the architecture to improve the individual fusion performance. Gabor filter technique is used for recognition of features from input image and the database images. To our knowledge this is the first visual, thermal and fused-data (fusion of visual and thermal data) face recognition fusion recognition system, which utilizes Gabor filter for feature extraction. We have achieved the accuracy of above 98%. Paper also discusses the performance issues of memory and response time and defines new frontiers for fast and efficient recognition system.