关键词: 图像预评估, 实时虹膜识别系统, 支持向量机联合评估网络, 坏样本

Abstract: 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.

Key words: Image pre-estimation, Real-time iris identification system, SVM associated pre-estimation network, Bad-image