论文标题
在有限的注释数据下，无监督的预训练，纹理意识和轻巧的模型，用于深度学习的IRIS识别
Unsupervised Pre-trained, Texture Aware And Lightweight Model for Deep Learning-Based Iris Recognition Under Limited Annotated Data
论文作者
论文摘要
在本文中，我们为虹膜识别提供了一个纹理意识到的轻巧的深度学习框架。我们的贡献主要是三倍。首先，为了解决被标记的虹膜数据的缺乏，我们提出了一个重建损失，以无监督的预训练阶段进行了指导，然后进行了监督改进。这将驱动网络权重以专注于歧视性虹膜纹理模式。接下来，我们建议在卷积神经网中进行一些纹理意识即兴表演，以更好地利用虹膜纹理。最后，我们表明，我们的系统培训和体系结构选择使我们能够设计一个比当代深度学习基线的有效框架，其参数少100倍，但可以对内部和交叉数据集评估获得更好的识别性能。
In this paper, we present a texture aware lightweight deep learning framework for iris recognition. Our contributions are primarily three fold. Firstly, to address the dearth of labelled iris data, we propose a reconstruction loss guided unsupervised pre-training stage followed by supervised refinement. This drives the network weights to focus on discriminative iris texture patterns. Next, we propose several texture aware improvisations inside a Convolution Neural Net to better leverage iris textures. Finally, we show that our systematic training and architectural choices enable us to design an efficient framework with upto 100X fewer parameters than contemporary deep learning baselines yet achieve better recognition performance for within and cross dataset evaluations.
