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Performance of biometric-based recognition systems depends on various factors: database quality, image preprocessing, encoding techniques, etc. Given a biometric database and a selected encoding method, the recognition capability of a system is limited by the relationship between the number of classes that the recognition system can encode and the length of encoded data describing the template at a specific level of distortion. In this paper, we evaluate empirical recognition capacity of biometric systems under the constraint of two global encoding techniques: principal component analysis (PCA) and independent component analysis (ICA). The developed methodology is applied to predict the capacity of different recognition channels formed during the acquisition of different iris and face databases. The proposed approach relies on data modeling and involves classical detection and information theories. The major contribution is in providing a guideline on how to evaluate capabilities of large-scale biometric recognition systems that are based on PCA and ICA encoding. Recognition capacity can also be promoted as a global quality measure of biometric databases.