There exist frequently different types of bad sample images in an iris identification application system. When these bad images are imported into the identification process, generally it results in increased enrollment failure rate and localization errors or identification errors. According to the articulation and resolution of the iris part, previous image quality evaluation methods estimate whether an image is a bad or not after having calculated the iris location of an input image. So, only part of bad samples can be handled, and it is time-consuming. The reasons and characteristics that bad sample images were analyed. A real-time pre-estimation method for supporting vector machine's associated estimation network was proposed. Before the localization or rough localization process, sample images temporarily saved in memory are detected. According to the output results from pre-estimation network, the system determines re-acquisition or to turn into the next step. The experimental result shows that the method can detect most types of the bad sample images. Detection speed is fast and error rate is comparatively low. The method can satisfy the pre-estimation requirements of a real-time iris identification system.

Convolution integral and convolution sum are principal methods in the linear time invariant systems analysis. At present, many text books that have been published in the my home country or foreign country, especially the Signals and Systems writing by Oppenheim A V and Signals and Systems writing by Haykin Simon, introduced the methods by use of the graph to determine the up limit, low limit and the exist interval of the integral or convolution sum. The closed methods that evaluate the convolution integral and convolution sum avoid to determine the up, low limits and the duration of the convolution integral and convolution sum by means of the graph，So that, the convolution integral and convolution sum according to the definition can be evaluated directly.