Low Computational Iris Recognition Based on Moving Average Filter

A moving average filter averages a number of input samples and produce a single output sample. This averaging action removes the high frequency components present in the signal. Moving average filters are normally used as low pass filters. In recursive filtering algorithm, previous output samples also are taken for averaging. This is the reason why its impulse response extends to infinity. We have developed a low computational approach for iris recognition based on 1D moving average filter. Simple averaging is used to reduce the effects of noise and a significative improvement in computational efficiency can be achieved if we perform the calculation of the mean in a recursive fashion.