###### Abstract  
Fingermarks play an important role in document identification. At the same time, fingermarks on paper documents are often accompanied by signatures and background text, which introduce noise to the original fingermark textures and increase the difficulty of detection. A signed fingermark detection method based on deep residual networks and a decision-level fusion strategy was proposed to defend against spoofing attacks from fake fingermarks. Firstly, the multi-scale structure was introduced in the residual module, which improved the network's depth and breadth without increasing the parameters. Then, the multi-probability label strategy was refined and employed to enhance the local encoding ability of the feature extraction. A score fusion strategy was designed, with weights allocated based on the difference in signed interference levels of local image blocks. Finally, a model fusion strategy based on evidence theory was suggested, which improved detection accuracy by leveraging complementarity between models. A large-scale fingermark database was established, which included real fingermarks made from real fingers and fake fingermarks made from various materials, and this was divided into two sub databases: signed and unsigned. The experimental results show that the proposed method achieves 96.16% accuracy based on the fingerprint dataset of the global liveness detection competition called LivDet2017 and achieves 99.30% accuracy based on the signed fingermark database, while it has good resistance to spoofing attacks from unknown materials.  
biometric recognition; signed fingermark; liveness detection; deep residual network; decision-level fusion +
Footnote †: journal: Medical  
effect. In particular, fingermarks have been employed in private lending and borrowing, such as on the occasion of financing between two natural persons, based on concerns regarding the easy imitation of and the uniqueness of fingermarks, often in accordance with the traditional custom of the mixed use of signatures and fingermarks. This type of fingermark is often accompanied by signatures, text and other background noise interference, also known as signed fingermarks, and the detection of signed fingermarks is thus more difficult. Fake fingermarks can lead to false convictions, not only causing personal losses, but also affecting the credibility of justice. Traditional fingerprint authentication mainly uses manual identification methods based on expert experience, which is highly subjective and time-consuming. The existing literature on computer-based fingermark liveness detection methods is sparse, and the detection accuracy is relatively low. Based on this status quo, it is necessary to further investigate high-performance signed fingermark liveness detection methods.  
The main aims of this paper are as follows:  
1. To propose a network model, SFNet (Signed Fingermark Net), based on deep residual networks and a multi-probability label classification strategy, which achieves the purpose of signed fingermark liveness detection and achieves high accuracy on a fingermark dataset.
2. To design and combine a multi-score fusion strategy based on the quality weights of local blocks of fingermark and a model fusion strategy based on evidence theory to further improve the detection accuracy.