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Palmprint orientation field recovery via attention-based generative adversarial network
Neurocomputing ( IF 6 ) Pub Date : 2021-01-18 , DOI: 10.1016/j.neucom.2021.01.049
Bing Liu , Jufu Feng

Orientation field is the key foundation of palmprint feature extraction and recognition. However, due to the presence of numerous wide creases, the palmprint orientation field can hardly be accurately estimated by previous methods, especially in the thenar region, which still faces huge challenges. To solve this problem, we formulate palmprint orientation field recovery as an inpainting task and propose a palmprint orientation field recovery model named attention-based generative adversarial network. The deep generative architecture provides a powerful representation and an attention module guides the network to adaptively focus on the inpainting region. To avoid manually marking orientation field, we design a quality evaluation module to iteratively obtain pseudo labels for model training and incorporate palmprint abundant prior knowledge as extra supervision information. Palmprint identification results on public THUPALMLAB palmprint database show that our proposed algorithm improves the rank-1 recognition rate from 91.7% to 99.3%, which significantly outperforms the state-of-the-arts. Besides, we also compare the sensitivity of our algorithm to different optimization methods and noise distributions. Robust performance provides a reliable evidence for law enforcement and biometric identification.



中文翻译:

通过基于注意力的生成对抗网络恢复掌纹方向场

方向场是掌纹特征提取和识别的关键基础。然而,由于存在许多宽的折痕,因此难以通过先前的方法来准确地估计掌纹取向场,特别是在仍面临巨大挑战的纳纳尔地区。为了解决该问题,我们将掌纹方向场恢复公式化为修复任务,并提出了一种基于注意力的生成对抗网络的掌纹方向场恢复模型。深度生成体系结构提供了强大的表示形式,并且关注模块引导网络自适应地专注于修复区域。为避免手动标记方向字段,我们设计了一个质量评估模块,以迭代方式获得用于模型训练的伪标签,并结合掌纹丰富的先验知识作为额外的监督信息。在公共THUPALMLAB掌纹数据库上的掌纹识别结果表明,我们提出的算法将1级识别率从91.7%提高到99.3%,明显优于最新技术。此外,我们还比较了算法对不同优化方法和噪声分布的敏感性。强大的性能为执法和生物识别提供了可靠的证据。此外,我们还比较了算法对不同优化方法和噪声分布的敏感性。强大的性能为执法和生物识别提供了可靠的证据。此外,我们还比较了算法对不同优化方法和噪声分布的敏感性。强大的性能为执法和生物识别提供了可靠的证据。

更新日期:2021-02-08
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