ROSE: Real One-Stage Effort to Detect the Fingerprint Singular Point Based on Multi-scale Spatial Attention
This work addresses the need for more accurate and efficient fingerprint recognition for security applications, though it is incremental as it builds on existing deep learning approaches.
The paper tackles the problem of inefficient and inaccurate fingerprint singular point detection by proposing ROSE, a real one-stage deep learning method that achieves higher detection rates and faster speeds on FVC2002 DB1 and NIST SD4 datasets.
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.