Background Fingerprint biometrics play an essential role in authentication. It remains a challenge to match fingerprints with the minutiae or ridges missing. Many fingerprints failed to match their targets due to the incompleteness. Result In this work, we modeled the fingerprints with Bezier curves and proposed a novel algorithm to detect and restore fragmented ridges in incomplete fingerprints. In the proposed model, the Bezier curves’ control points represent the fingerprint fragments, reducing the data size by 89% compared to image representations. The representation is lossless as the restoration from the control points fully recovering the image. Our algorithm can effectively restore incomplete fingerprints. In the SFinGe synthetic dataset, the fingerprint image matching score increased by an average of 39.54%, the ERR (equal error rate) is 4.59%, and the FMR1000 (false match rate) is 2.83%, these are lower than 6.56% (ERR) and 5.93% (FMR1000) before restoration. In FVC2004 DB1 real fingerprint dataset, the average matching score increased by 13.22%. The ERR reduced from 8.46% before restoration to 7.23%, and the FMR1000 reduced from 20.58 to 18.01%. Moreover, We assessed the proposed algorithm against FDP-M-net and U-finger in SFinGe synthetic dataset, where FDP-M-net and U-finger are both convolutional neural network models. The results show that the average match score improvement ratio of FDP-M-net is 1.39%, U-finger is 14.62%, both of which are lower than 39.54%, yielded by our algorithm. Conclusions Experimental results show that the proposed algorithm can successfully repair and reconstruct ridges in single or multiple damaged regions of incomplete fingerprint images, and hence improve the accuracy of fingerprint matching.
Smart abnormal emotion analysis refers to identifying abnormal sentiments, opinions, or attitudes from massive patterns automatically. The abnormal emotion may be hidden in a paragraph of text to reflect the sentiment suddenly changes. The suddenly changed emotion should be identified in time to avoid severe consequence. For instance, a customer would not buy a product any more if he or she has a negative emotion to this product. With the popularization of social media, more and more information is available. For example, feedbacks, comments, or opinions widely exist on Twitter. Identifying abnormal emotion by analyzing texts in social media becomes a hot topic, which enables companies or government organizations to take prevention strategies in time if needing. In this paper, we propose a multivariate Gaussian model based abnormal emotion detection method. In multivariate Gaussian model, whether a user has abnormal emotion is determined by the joint probability density. The distribution test shows that the negative, positive, and neutral emotions of a user follow a normal distribution, while the surprise and anger emotions do not follow. The emotions of texts from social media posted by a group follow a “power law distribution”, while the individual users do not. The abnormal emotion can be detected by multivariate Gaussian about 84.60%.
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