论文标题
不受约束的眼周识别：使用生成深度学习框架来归功于归一化
Unconstrained Periocular Recognition: Using Generative Deep Learning Frameworks for Attribute Normalization
论文作者
论文摘要
在不受约束的环境中起作用的眼部生物识别系统通常面临着由多种因素共同降低所获得的数据质量的多种因素引起的小小的紧凑性的问题。在这项工作中，我们提出了一种基于深度学习生成框架的属性归一化策略，该策略降低了成对比较中使用的样品的可变性，而无需降低其可区分性。所提出的方法可以看作是一个预处理步骤，为数据正则化有助于提高识别精度，完全不可知所使用的识别策略。作为概念证明，我们考虑了“眼镜”和“凝视”因素，并在不使用建议的归一化策略的情况下比较了五种不同识别方法的性能水平。另外，我们引入了一个新的数据集，以实现不受约束的眼周识别，该数据集由移动设备获得的图像组成，特别适合感知“佩戴眼镜”对识别效果的影响。我们的实验是在两个不同的数据集中进行的，并支持我们属性归一化方案的有用性以提高识别性能。
Ocular biometric systems working in unconstrained environments usually face the problem of small within-class compactness caused by the multiple factors that jointly degrade the quality of the obtained data. In this work, we propose an attribute normalization strategy based on deep learning generative frameworks, that reduces the variability of the samples used in pairwise comparisons, without reducing their discriminability. The proposed method can be seen as a preprocessing step that contributes for data regularization and improves the recognition accuracy, being fully agnostic to the recognition strategy used. As proof of concept, we consider the "eyeglasses" and "gaze" factors, comparing the levels of performance of five different recognition methods with/without using the proposed normalization strategy. Also, we introduce a new dataset for unconstrained periocular recognition, composed of images acquired by mobile devices, particularly suited to perceive the impact of "wearing eyeglasses" in recognition effectiveness. Our experiments were performed in two different datasets, and support the usefulness of our attribute normalization scheme to improve the recognition performance.
