Off-angle iris recognition using single and multiple deep learning frameworks
Off-angle iris recognition using single and multiple deep learning frameworks
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DOI：
10.1117/12.2662862
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发表时间：
2023-06
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通讯作者：
David Chavarro;M. Karakaya
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作者：
David Chavarro;M. Karakaya
虹膜识别是一种广泛使用的生物识别技术，在良好控制的环境中具有高准确性和可靠性。然而，在非理想的情况下，识别精度可能会显着降低，例如偏离角度的虹膜图像。为了应对这些挑战，已经提出了深度学习框架，以通过他们的偏离角度虹膜图像来识别主体。传统的基于CNN的虹膜识别系统使用同一主题的多个偏离角度的虹膜图像来训练单个深度网络以提取注视不变特征，并使用该单个网络来测试传入的偏离角度图像以将其分类到相同的主题类别。在另一种方法中，针对每个注视角度训练多个浅层网络，这些浅层网络将是特定注视角度的专家。当测试一个偏离角度的虹膜图像时，我们首先估计注视角度，并将探针图像馈送到其相应的网络进行识别。在本文中，我们分析了单模式和多模式深度学习框架的性能，以通过他们的偏角虹膜图像来识别受试者。具体来说，我们比较了单个AlexNet与多个SqueezeNet模型的性能。SqueezeNet是AlexNet的变体，使用的参数少50倍，并针对计算资源有限的设备进行了优化。使用多个浅层网络的多模型方法，其中每个网络都是特定注视角度的专家。我们的实验是在一个由100名受试者组成的偏角虹膜数据集上进行的，这些受试者以10度的间隔在-50到+50度之间捕获。结果表明，与训练的角度更远的角度比更接近训练的注视角度的角度具有更低的模型准确度。我们的研究结果表明，使用SqueezeNet（比AlexNet需要更少的参数）可以在计算资源有限的设备上实现虹膜识别，同时保持准确性。总的来说，本研究的结果可以有助于开发更强大的虹膜识别系统，可以在非理想的情况下表现良好。
Iris recognition is a widely used biometric technology that has high accuracy and reliability in well-controlled environments. However, the recognition accuracy can significantly degrade in non-ideal scenarios, such as off-angle iris images. To address these challenges, deep learning frameworks have been proposed to identify subjects through their off-angle iris images. Traditional CNN-based iris recognition systems train a single deep network using multiple off-angle iris image of the same subject to extract the gaze invariant features and test incoming off-angle images with this single network to classify it into same subject class. In another approach, multiple shallow networks are trained for each gaze angle that will be the experts for specific gaze angles. When testing an off-angle iris image, we first estimate the gaze angle and feed the probe image to its corresponding network for recognition. In this paper, we present an analysis of the performance of both single and multimodal deep learning frameworks to identify subjects through their off-angle iris images. Specifically, we compare the performance of a single AlexNet with multiple SqueezeNet models. SqueezeNet is a variation of the AlexNet that uses 50x fewer parameters and is optimized for devices with limited computational resources. Multi-model approach using multiple shallow networks, where each network is an expert for a specific gaze angle. Our experiments are conducted on an off-angle iris dataset consisting of 100 subjects captured at 10-degree intervals between -50 to +50 degrees. The results indicate that angles that are more distant from the trained angles have lower model accuracy than the angles that are closer to the trained gaze angle. Our findings suggest that the use of SqueezeNet, which requires fewer parameters than AlexNet, can enable iris recognition on devices with limited computational resources while maintaining accuracy. Overall, the results of this study can contribute to the development of more robust iris recognition systems that can perform well in non-ideal scenarios.