Biometric templates are often used in intelligent human computer interaction systems that include automated access control and personalization of user interaction. The effectiveness of biometric systems is directly linked with aging that causes modifications on biometric features. For example the long term performance of person identification systems decreases as biometric templates derived from aged subjects may display substantial differences when compared to reference templates whereas in age estimation, aging variation allows the age of a subject to be estimated. In this paper we attempt to quantify the effects of aging for different biometric modalities facilitating in that way the design of systems that use biometric features. In this context the homogeneity of statistical distributions of biometric features belonging to certain age classes is quantified enabling in that way the definition of age sensitive and age invariant biometric features. Experimental results demonstrate the applicability of the method in quantifying aging effects.