This paper proposes a human thermal face recognition approach with two variants based on Random linear
Oracle (RLO) ensembles. For the two approaches, the Segmentation-based Fractal Texture Analysis (SFTA) algorithm was used for extracting features and the RLO ensemble classifier was used for recognizing the face from its thermal image. For the dimensionality reduction, one variant (SFTALDA-RLO) was used the technique of Linear Discriminant Analysis (LDA) while the other variant (SFTA-PCA-RLO) was used the Principal Component Analysis (PCA). The classifier’s model was built using the RLO classifier during the training phase and in the testing phase then this model was used to identify the unknown sample images. The two variants were evaluated using the Terravic Facial IR Database and the experimental results showed that the two variants achieved a good recognition rate at 94.12% which is better than related work.

A simple and efficient approach for profiling the greenness of high performance liquid chromatography (HPLC) methods is presented. This environmental assessment tool (EAT) takes into consideration the environmental, health and safety issues for all solvents involved in the chromatographic method, and calculates a total score that can be used for comparison of the greenness of different methods. A software, HPLC-EAT, has been designed to facilitate the calculation and can be downloaded free of charge at http://www.biotek.lu.se/hplc-eat/. HPLC-EAT was successfully applied for a set of different HPLC methods from the literature, including both analytical and preparative chromatography. The performance of the tool was validated and it was further combined with another free software Eco-solvent tool to perform life cycle assessments of waste disposal options of distillation or incineration. HPLC-EAT can be routinely used in method development to calculate the greenness beside the conventional standards of accuracy, robustness and reproducibility.