{"title":"基于深度学习的潜在指纹图像质量评估","authors":"J. Ezeobiejesi, B. Bhanu","doi":"10.1109/CVPRW.2018.00092","DOIUrl":null,"url":null,"abstract":"Latent fingerprints are fingerprint impressions unintentionally left on surfaces at a crime scene. They are crucial in crime scene investigations for making identifications or exclusions of suspects. Determining the quality of latent fingerprint images is crucial to the effectiveness and reliability of matching algorithms. To alleviate the inconsistency and subjectivity inherent in feature markups by latent fingerprint examiners, automatic processing of latent fingerprints is imperative. We propose a deep neural network that predicts the quality of image patches extracted from a latent fingerprint and knits them together to predict the quality of a given latent fingerprint. The proposed approach eliminates the need for manual ROI markup and manual feature markup by latent examiners. Experimental results on NIST SD27 show the effectiveness of our technique in latent fingerprint quality prediction.","PeriodicalId":150600,"journal":{"name":"2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"14","resultStr":"{\"title\":\"Latent Fingerprint Image Quality Assessment Using Deep Learning\",\"authors\":\"J. Ezeobiejesi, B. Bhanu\",\"doi\":\"10.1109/CVPRW.2018.00092\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Latent fingerprints are fingerprint impressions unintentionally left on surfaces at a crime scene. They are crucial in crime scene investigations for making identifications or exclusions of suspects. Determining the quality of latent fingerprint images is crucial to the effectiveness and reliability of matching algorithms. To alleviate the inconsistency and subjectivity inherent in feature markups by latent fingerprint examiners, automatic processing of latent fingerprints is imperative. We propose a deep neural network that predicts the quality of image patches extracted from a latent fingerprint and knits them together to predict the quality of a given latent fingerprint. The proposed approach eliminates the need for manual ROI markup and manual feature markup by latent examiners. Experimental results on NIST SD27 show the effectiveness of our technique in latent fingerprint quality prediction.\",\"PeriodicalId\":150600,\"journal\":{\"name\":\"2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)\",\"volume\":\"4 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"14\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CVPRW.2018.00092\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CVPRW.2018.00092","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Latent Fingerprint Image Quality Assessment Using Deep Learning
Latent fingerprints are fingerprint impressions unintentionally left on surfaces at a crime scene. They are crucial in crime scene investigations for making identifications or exclusions of suspects. Determining the quality of latent fingerprint images is crucial to the effectiveness and reliability of matching algorithms. To alleviate the inconsistency and subjectivity inherent in feature markups by latent fingerprint examiners, automatic processing of latent fingerprints is imperative. We propose a deep neural network that predicts the quality of image patches extracted from a latent fingerprint and knits them together to predict the quality of a given latent fingerprint. The proposed approach eliminates the need for manual ROI markup and manual feature markup by latent examiners. Experimental results on NIST SD27 show the effectiveness of our technique in latent fingerprint quality prediction.