Iris Recognition With Off-the-Shelf CNN Features: A Deep Learning Perspective
Iris Recognition With Off-the-Shelf CNN Features: A Deep Learning Perspective
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DOI：
10.1109/access.2017.2784352
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发表时间：
2018-01-01
期刊：
影响因子：
3.9
通讯作者：
Sridharan, Sridha
中科院分区：
文献类型：
--
作者：
Nguyen, Kien;Fookes, Clinton;Sridharan, Sridha
虹膜识别是指根据虹膜图案识别个体的自动化过程。虹膜基质看似随机的性质使其成为生物特征识别的独特线索。通过将个体虹膜图案的纹理细微差别投影到 Gabor 小波上并将随后的相量响应转换为二进制代码，可以有效地提取和编码个体虹膜图案的纹理细微差别 - 这是道格曼首创的技术。据观察，该纹理描述符是一种鲁棒的特征描述符，具有非常低的错误匹配率和低计算复杂度。然而，深度学习和计算机视觉的最新进展表明，使用卷积神经网络（CNN）提取的通用描述符能够表示复杂的图像特征。鉴于 CNN 在 ImageNet 大规模视觉识别挑战和大量其他计算机视觉任务上的卓越性能，在本文中，我们探讨了最先进的预训练 CNN 在虹膜识别上的性能。我们表明，现成的 CNN 特征虽然最初是为了对通用对象进行分类而训练的，但也非常擅长表示虹膜图像，有效地提取有区别的视觉特征，并在两个虹膜数据集：ND-CrossSensor-2013 和 CASIA-Iris-Thousand 上取得了有希望的识别结果。我们还讨论了利用深度学习方法解决虹膜识别问题的挑战和未来的研究方向。
Iris recognition refers to the automated process of recognizing individuals based on their iris patterns. The seemingly stochastic nature of the iris stroma makes it a distinctive cue for biometric recognition. The textural nuances of an individual's iris pattern can be effectively extracted and encoded by projecting them onto Gabor wavelets and transforming the ensuing phasor response into a binary code - a technique pioneered by Daugman. This textural descriptor has been observed to be a robust feature descriptor with very low false match rates and low computational complexity. However, recent advancements in deep learning and computer vision indicate that generic descriptors extracted using convolutional neural networks (CNNs) are able to represent complex image characteristics. Given the superior performance of CNNs on the ImageNet large scale visual recognition challenge and a large number of other computer vision tasks, in this paper, we explore the performance of state-of-the-art pre-trained CNNs on iris recognition. We show that the off-the-shelf CNN features, while originally trained for classifying generic objects, are also extremely good at representing iris images, effectively extracting discriminative visual features and achieving promising recognition results on two iris datasets: ND-CrossSensor-2013 and CASIA-Iris-Thousand. We also discuss the challenges and future research directions in leveraging deep learning methods for the problem of iris recognition.