In the context of the rapid development of science and technology and the modernization of the legal system, criminal activities are becoming more and more intelligent and technological, which also puts forward higher...
详细信息
In the context of the rapid development of science and technology and the modernization of the legal system, criminal activities are becoming more and more intelligent and technological, which also puts forward higher requirements for criminal technology. The current criminal technology equipment is relatively backward, and the technical level is not high enough, resulting in a low utilization rate of trace material evidence extraction, which directly affects the role of criminal technology in the investigation and solving of cases. In recent years, fingerprint recognition algorithms and image edge detection algorithms have been widely used in various fields. This work studied the application of fingerprint image fuzzy edge recognitionalgorithm in criminal technology, in order to improve the level of criminal technology and the utilization rate of physical evidence extraction. The criminal technology system is upgraded and optimized by combining fingerprint recognition algorithm and image edge detection algorithm. And fuzzy theory is added to ensure the feasibility of the research. The experimental results show that the fuzzy edge recognitionalgorithm of fingerprint image can improve the level of criminal technology and the utilization rate of material evidence to a certain extent. The utilization rate is increased by 7.04%. The recognition accuracy of the fuzzy recognition method is also 13.2% higher than that of the methods in the literature.
In this study, the authors show that by the current state-of-the-art synthetically generated fingerprints can easily be discriminated from real fingerprints. They propose a non-parametric distribution-based method usi...
详细信息
In this study, the authors show that by the current state-of-the-art synthetically generated fingerprints can easily be discriminated from real fingerprints. They propose a non-parametric distribution-based method using second-order extended minutiae histograms (MHs) which can distinguish between real and synthetic prints with very high accuracy. MHs provide a fixed-length feature vector for a fingerprint which are invariant under rotation and translation. This 'test of realness' can be applied to synthetic fingerprints produced by any method. In this study, tests are conducted on the 12 publicly available databases of FVC2000, FVC2002 and FVC2004 which are well established benchmarks for evaluating the performance of fingerprint recognition algorithms;3 of these 12 databases consist of artificial fingerprints generated by the SFinGe software. In addition, they evaluate the discriminative performance on a database of synthetic fingerprints generated by the software of Bicz against real fingerprint images. They conclude with suggestions for the improvement of synthetic fingerprint generation.
暂无评论