论文标题
玫瑰：基于多尺度空间注意的指纹奇异点的真正单阶段努力
ROSE: Real One-Stage Effort to Detect the Fingerprint Singular Point Based on Multi-scale Spatial Attention
论文作者
论文摘要
准确有效地检测到奇异点是指纹识别的最重要任务之一。近年来，深度学习已逐渐用于指纹奇异点检测中。但是，当前基于深度学习的奇异点检测方法是两阶段或多阶段，这使它们既耗时又耗时。更重要的是，它们的检测准确性尚不令人满意，尤其是在低质量指纹的情况下。在本文中，我们做出了一个真正的一阶段努力，以更准确，更有效地检测指纹奇异点，因此我们将所提出的算法命名为简短，其中多规模的空间注意力，高斯热图和焦点损失的变体被同时应用在一起以达到更高的检测率。数据集FVC2002 DB1和NIST SD4上的实验结果表明，我们的玫瑰在检测率，错误警报率和检测速度方面优于最先进的算法。
Detecting the singular point accurately and efficiently is one of the most important tasks for fingerprint recognition. In recent years, deep learning has been gradually used in the fingerprint singular point detection. However, current deep learning-based singular point detection methods are either two-stage or multi-stage, which makes them time-consuming. More importantly, their detection accuracy is yet unsatisfactory, especially in the case of the low-quality fingerprint. In this paper, we make a Real One-Stage Effort to detect fingerprint singular points more accurately and efficiently, and therefore we name the proposed algorithm ROSE for short, in which the multi-scale spatial attention, the Gaussian heatmap and the variant of focal loss are applied together to achieve a higher detection rate. Experimental results on the datasets FVC2002 DB1 and NIST SD4 show that our ROSE outperforms the state-of-art algorithms in terms of detection rate, false alarm rate and detection speed.
