Human Thermal Face Recognition Based on Random Linear Oracle (RLO) Ensembles

This paper proposes a human thermal face recognitionapproach with two variants based on Random linearOracle (RLO) ensembles. For the two approaches, the Segmentation-based Fractal Texture Analysis (SFTA) algorithmwas used for extracting features and the RLO ensembleclassifier was used for recognizing the face from its thermalimage. For the dimensionality reduction, one variant (SFTALDA-RLO) was used the technique of Linear DiscriminantAnalysis (LDA) while the other variant (SFTA-PCA-RLO) wasused the Principal Component Analysis (PCA). The classifier'smodel was built using the RLO classifier during the trainingphase and in the testing phase then this model was usedto identify the unknown sample images. The two variantswere evaluated using the Terravic Facial IR Database and theexperimental results showed that the two variants achieved agood recognition rate at 94.12% which is better than related work.