A method of fingermark anti-counterfeiting for forensic document identification

Automatic fingerprint identification is a key technology of biological attribute measurement. Due to its sensitivity to fake fingerprint attack, its security has been widely concerned. The fingermark is an important evidence for forensic documents identification. The existence of fake fingermark seriously threatens the fairness and legitimacy in the process of forensic identification. In this paper, in view of the security risks existing in the fingermark identification of forensic documents, a database named JLW-FM-DB is introduced for detecting genuine and fake fingermarks, which consists of two sub databases, signed and unsigned, each of which covers common fake fingermark materials. Based on the database, this paper proposes a method of fingermark anti-counterfeiting based on convolution neural network(CNN). A Patch-Label training strategy is proposed, which uses the unified label as the supervision signal for the class heatmap output by the last convolution layer. This strategy realizes stronger local supervision ability to input fingermark image and enhances local coding ability of CNN feature extraction. The experiments show that our methods are suitable for the detection of genuine and fake fingermarks, achieving 97.999% average accuracy in the signed fingermark database. The effectiveness of Patch-Label training strategy in fingermark anti-counterfeiting is also proved. Moreover, a model fusion of different models can further improve the detection ability of genuine and fake fingermarks, reaching to 98.496% average accuracy.