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<p>Segmentation Techniques through Machine Based Learning for Latent Fingerprint Indexing and Identification</p>

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Title Statement <p>Segmentation Techniques through Machine Based Learning for Latent Fingerprint Indexing and Identification</p>
 
Added Entry - Uncontrolled Name Singh, Harivans Pratap; Department of Computer Science & Engineering ABES Engineering College, Ghaziabad, AKTU, Lucknow, India
Dimri, Priti ; Department of Computer Science and Applications, G.B. Pant Engineering College, Ghurdauri, Uttarakhand, India,
Tiwari, Shailesh ; ABES Engineering College ,Ghaziabad, AKTU, Lucknow, India
Saraswat, Manish ; ABES Engineering College ,Ghaziabad, AKTU, Lucknow, India
 
Uncontrolled Index Term Average indexed time, Global structure matching, Gradient, Machine learning, Translational features
 
Summary, etc. <p>Latent fingerprints have become most important evidence in law enforcement department and forensic agencies worldwide. It is also very important evidence in forensic applications to identify criminals as it is mostly encountered in crime scenes. Segmentation is one of the solutions to extract quality features. Fingerprint indexing reduces the search space without compromising accuracy. In this paper, minutiae based rotational and translational features and a global matching approach in combination with local matching is used in order to boost the indexing efficiency. Also, a machine learning (ML) based segmentation model is designed as a binary classification model to classify local blocks into foreground and background. Average indexed time as well as accuracy for full as well as partial fingerprints is tabulated by varying the template sminutiae.</p>
 
Publication, Distribution, Etc. Journal of Scientific & Industrial Research
2022-11-19 05:33:33
 
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http://op.niscair.res.in/index.php/JSIR/article/view/68640
 
Data Source Entry Journal of Scientific & Industrial Research; ##issue.vol## 79, ##issue.no## 3 (2020): Journal of Scientific & Industrial Research
 
Language Note en