Computer graphics (2nd ed.): C version
Computer graphics (2nd ed.): C version
Wavelets for Computer Graphics: A Primer, Part 1
IEEE Computer Graphics and Applications
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)
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
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)
Recent advances in visual and infrared face recognition: a review
Computer Vision and Image Understanding
Digital Image Processing (3rd Edition)
Digital Image Processing (3rd Edition)
Pose-Invariant Physiological Face Recognition in the Thermal Infrared Spectrum
CVPRW '06 Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop
Journal of Cognitive Neuroscience
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Thermal infrared (IR) images focus on changes of temperature distribution on facialmuscles and blood vessels. These temperature changes can be regarded as texture features of images. A comparative study of face two recognition methods working in thermal spectrum is carried out in this paper. In the first approach, the training images and the test images are processed with Haar wavelet transform and the LL band and the average of LH/HL/HH bands subimages are created for each face image. Then a total confidence matrix is formed for each face image by taking a weighted sum of the corresponding pixel values of the LL band and average band. For LBP feature extraction, each of the face images in training and test datasets is divided into 161 numbers of subimages, each of size 8 × 8 pixels. For each such subimages, LBP features are extracted which are concatenated in manner. PCA is performed separately on the individual feature set for dimensionality reduction. Finally, two different classifiers namely multilayer feed forward neural network and minimum distance classifier are used to classify face images. The experiments have been performed on the database created at our own laboratory and Terravic Facial IR Database.