A target image–oriented dictionary learning–based method for fully automated latent fingerprint forensic. (8th May 2018)

• Record Type: Journal Article
• Authors: Xu, Jinwei; Hu, Jiankun; Jia, Xiuping
• Is Part Of: Computational intelligence. Volume 34:Number 4(2018)
• Page Start: 1178
• Page End: 1198
• Publication Date: 2018-05-08
• Subjects: dictionary learning -- fingerprint segmentation -- latent fingerprint; Artificial intelligence -- Periodicals; Computational linguistics -- Periodicals; 006.3
• DOI: 10.1111/coin.12177
• Languages: English
• ISSNs: 0824-7935

Abstract

Abstract: Several fully automated latent print forensic techniques have been reported. In this paper, we propose a fully automated latent print segmentation module for the partition of the fingerprint region in a query latent image, which can help find a corresponding match of the suspect reliably. Being different from the existing methods that build the prelearned dictionary from the high‐quality fingerprint image patches, the proposed dictionary learning procedure is conducted on the target images. The advantages of the proposed method are the following: (i) it does not require a large number of high‐quality "ridge‐valley" atoms and (ii) not only the structure similarity but also the pattern scale has been kept consistent between the target image patches and learned dictionary atoms. Because no commercial latent fingerprint matcher is publicly available and the latent matcher reported in the literature is not accessible to the public either, a latent fingerprint matching platform is implemented to evaluate the obtained segmentation results and automated latent fingerprint matching performance. On the basis of this platform, experimental comparisons are conducted to assess closeness to the system performance upper bound when different segmentation modules are deployed. Moreover, matcher‐independent criteria such as genuine minutiae preservation rate and the segmented region of interest's accuracy are used. All the experimental results demonstrate that the proposed segmentation approach outperforms state‐of‐the‐art techniques in terms of finding the correct fingerprint of the suspect subject.