In this paper, we propose a new feature extraction method based on independent component analysis (ICA) for iris recognition, which is known as the most reliable biometric system. We extract iris features using a bank of filters which are selected from the ICA basis functions. The ICA basis functions themselves are sufficient to be used as filter kernels for extracting iris features because they are estimated by training iris signals. Using techniques of the ICA estimation, we generate many kinds of candidates ICA filters. To select the ICA filters for extracting salient features efficiently, we introduce the requirements of the ICA filter. Each ICA filter has a different filter size and a good discrimination power to identify iris pattern. Also, the correlation between bandwidths of the ICA filters is minimized. Experimental results show that the EER of proposed ICA filter bank is better than those of existing methods in both the Yonsei iris database and CASIA iris database.