Periocular Recognition Using CNN Features Off-the-Shelf
Periocular Recognition Using CNN Features Off-the-Shelf
复制标题
使用现成的 CNN 功能进行眼周识别
DOI：
10.23919/biosig.2018.8553348
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发表时间：
2018
期刊：
影响因子：
--
通讯作者：
J. Bigün
中科院分区：
文献类型：
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作者：
Kevin Hernandez;F. Alonso;J. Bigün
眼周是指眼睛周围的区域，包括巩膜、眼睑、睫毛、眉毛和皮肤。它具有惊人的高分辨能力，是需要最少约束获取的眼形态。在这里，我们将现有的预训练架构（在ImageNet大规模视觉识别挑战的背景下提出）应用于眼周识别任务。事实证明，除了设计它们的检测和分类任务外，这些方法在许多其他计算机视觉任务中非常成功。实验是用数码相机拍摄的眼周图像数据库完成的。我们证明了这些现成的CNN特征可以有效地识别基于眼周图像的个体，尽管被训练为分类一般物体。与参考眼周特征相比，它们的EER降低了约40%，CNN和传统特征的融合提供了额外的改进。
Periocular refers to the region around the eye, including sclera, eyelids, lashes, brows and skin. With a surprisingly high discrimination ability, it is the ocular modality requiring the least constrained acquisition. Here, we apply existing pre-trained architectures, proposed in the context of the ImageNet Large Scale Visual Recognition Challenge, to the task of periocular recognition. These have proven to be very successful for many other computer vision tasks apart from the detection and classification tasks for which they were designed. Experiments are done with a database of periocular images captured with a digital camera. We demonstrate that these off-the-shelf CNN features can effectively recognize individuals based on periocular images, despite being trained to classify generic objects. Compared against reference periocular features, they show an EER reduction of up to ~40%, with the fusion of CNN and traditional features providing additional improvements.