Floating search methods in feature selection
Pattern Recognition Letters
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Detection Using Improved LBP under Bayesian Framework
ICIG '04 Proceedings of the Third International Conference on Image and Graphics
Machine assessment of neonatal facial expressions of acute pain
Decision Support Systems
Local binary patterns for a hybrid fingerprint matcher
Pattern Recognition
Review article: Touch-less palm print biometrics: Novel design and implementation
Image and Vision Computing
Machine recognition and representation of neonatal facial displays of acute pain
Artificial Intelligence in Medicine
Enhanced local texture feature sets for face recognition under difficult lighting conditions
AMFG'07 Proceedings of the 3rd international conference on Analysis and modeling of faces and gestures
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part II
Local binary patterns variants as texture descriptors for medical image analysis
Artificial Intelligence in Medicine
Face detection with the modified census transform
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Texture Description Through Histograms of Equivalent Patterns
Journal of Mathematical Imaging and Vision
Facial expression recognition based on adaptive weighted fusion histograms
ICIC'13 Proceedings of the 9th international conference on Intelligent Computing Theories and Technology
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This paper focuses on the use of image-based techniques for classifying pain states, in particular we compare several texture descriptors based on Local Binary Patterns (LBP), and we proposed some novel solutions based on the combination of new texture descriptors: the Elongated Ternary Pattern (ELTP) and the Elongated Binary Pattern (ELBP). ELTP is the best performing descriptor in our experiments. The ELBP descriptor combines characteristics of the Local Ternary Pattern (LTP) and ELTP. These two variants of the standard LBP are obtained by considering different shapes for the neighborhood calculation and different encodings for the evaluation of the local gray-scale difference. The resulting extracted features are used to train a support vector machine classifier. Extensive experiments are conducted using the Infant COPE database of neonatal facial images. Our results show that a local approach based on the ELTP feature extractor produces a reliable system for classifying pain states.