In the area of biometrics, face classification becomes one of the most appealing and commonly used approaches for personal identification. There has been an ongoing quest for designing systems that exhibit high classification rates and portray significant robustness. This feature becomes of paramount relevance when dealing with noisy and uncertain images. The design of face recognition classifiers capable of operating in presence of deteriorated (noise affected) face images requires a careful quantification of deterioration of the existing approaches vis-à-vis anticipated form and levels of image distortion. The objective of this experimental study is to reveal some general relationships characterizing the performance of two commonly used face classifiers (that is Eigenfaces and Fisherfaces) in presence of deteriorated visual information. The findings obtained in our study are crucial to identify at which levels of noise the face classifiers can still be considered valid. Prior knowledge helps us develop adequate face recognition systems. We investigate several typical models of image distortion such as Gaussian noise, salt and pepper, and blurring effect and demonstrate their impact on the performance of the two main types of the classifiers. Several distance models derived from the Minkowski family of distances are investigated with respect to the produced classification rates. The experimental environment concerns a well-known standard in this area of face biometrics such as the FERET database. The study reports on the performance of the classifiers, which is based on a comprehensive suite of experiments and delivers several design hints supporting further developments of face classifiers.