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In the absence of real data for extensive testing of newly designed large-scale biometrics recognition systems a number of solutions are possible including use of resampling methods, generation of synthetic data having properties similar to real data of interest, or use of analytical tools to predict the performance. Each of the methods has its own limitations. In this work, we focus on iris biometric. We briefly describe a model based approach to synthesize iris images and focus on performance comparison for synthesized and real iris images. Iris image processing assumes a traditional Gabor filter based encoding approach. Comparison of synthetic and real data is performed at three levels of processing: (1) image level, (2) texture level, and (3) decision level. The results indicate that in most cases the performance of synthesized iris images is comparable to the performance of the real iris images.