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Fusing the information in visible light and near-infrared images for iris recognition

机译：将信息融合到可见光和近红外图像中以进行虹膜识别

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Automated human identification is a significant issue in real and virtual societies. Iris is a suitable choice for meeting this goal. In this paper, we present an iris recognition system that uses images acquired in both near-infrared and visible lights. These two types of images reveal different textural information of the iris tissue. We demonstrated the necessity to process both VL and NIR images to recognize irides. The proposed system exploits two feature extraction algorithms: one is based on 1D log-Gabor wavelet which gives a detailed representation of the iris region and the other is based on 1D Haar wavelet which represents a coarse model of iris. The Haar wavelet algorithm is proposed in this paper. It makes smaller iris templates than the 1D log-Gabor approach and yet achieves an appropriate recognition rate. We performed the fusion at the match score level and examined the performance of the system in both verification and identification modes. UTIRIS database was used to evaluate the method. The results were compared with other approaches and proved to have better recognition accuracy, while no image enhancement technique is utilized prior to the feature extraction stage. Furthermore, we demonstrated that fusion can compensate the lack of input image information, which can be beneficial in reducing the computation complexity and handling non-cooperative iris images.
机译：在真实和虚拟社会中，自动人类识别是一个重要问题。虹膜是实现此目标的合适选择。在本文中，我们提出了一种虹膜识别系统，该系统使用在近红外和可见光下获取的图像。这两种类型的图像揭示了虹膜组织的不同纹理信息。我们证明了处理VL和NIR图像以识别铱的必要性。提出的系统利用两种特征提取算法：一种基于一维log-Gabor小波，它给出了虹膜区域的详细表示，另一种基于一维Haar小波，它代表了虹膜的一个粗略模型。提出了Haar小波算法。与一维log-Gabor方法相比，它可以使虹膜模板更小，并且可以实现适当的识别率。我们在比赛分数级别进行了融合，并在验证和识别模式下检查了系统的性能。使用UTIRIS数据库评估该方法。将结果与其他方法进行比较，并证明具有更好的识别精度，而在特征提取阶段之前未使用图像增强技术。此外，我们证明了融合可以弥补输入图像信息的不足，这对降低计算复杂性和处理非合作虹膜图像很有帮助。

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