In this work, Binary Local Patterns (LBP), Support Vector Machine (SVM) and Trade-off (TOF) correlation filter are evaluated in face recognition tasks using thermal infrared imagery. The infrared technology has a particular kind of noise called non-uniformity and correspond to a fixed pattern noise superimposed at the input image, degrading the quality of the scene. Non-uniformity varies over time very slowly, and in many applications, depending of the technology used, can be assumed constant for at least several hours. Additionally, additive Gaussian noise (variable over time) is generated by the associated electronics. Both kind of noise affect the performance of classifiers in face recognition applications using infrared technology and must be considered. The comparison of performance of each method considering fixed and variable over time noise leads allow to conclude that SVM is more robust under both kind of noise.