Improving the Efficiency of Automated Latent Fingerprint Identification Using Stack of Convolutional Auto-encoder

In this paper, a method for improving the efficiency of latent fingerprint segmentation and detection system is presented. Structural detection and precise segmentation of fingerprints otherwise not visible to the naked eye (called latents), provide the basis for automatic identification of latent f...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:International Conference on Parallel, Distributed and Grid Computing (PDGC ...) s. 191 - 196
Hlavní autoři: Chhabra, Megha, Shukla, Manoj Kumar, Ravulakollu, Kiran Kumar
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 06.11.2020
Témata:
Autoencoder Convolutional neural network
Classification
Computational modeling
Feature extraction
Fingerprint recognition
Image color analysis
Image processing
Image segmentation
Latent fingerprint
Load modeling
Segmentation
Training
ISSN:2573-3079
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Abstract In this paper, a method for improving the efficiency of latent fingerprint segmentation and detection system is presented. Structural detection and precise segmentation of fingerprints otherwise not visible to the naked eye (called latents), provide the basis for automatic identification of latent fingerprints. The method is based on the assumption, that including detection of relevant structure of interest from latent fingerprint image into an effective segmentation model pipeline improves the effectiveness of the model and efficiency of the automated segmentation. The approach discards detections of poor-quality due to noise, inadequate data, misplaced structures of interests from multiple instances of fingermarks in the image etc. A collaborative detector-segmentation approach is proposed which establishes reproducibility and repeatability of the model, consequently increasing the efficiency of the frame of work. The results are obtained on IIIT -DCLF database. Performing saliency-based detection using color based visual distortion reducing the subsequent information processing cost through a stack of the convolutional autoencoder. The results obtained signify significant improvement over published results.
AbstractList In this paper, a method for improving the efficiency of latent fingerprint segmentation and detection system is presented. Structural detection and precise segmentation of fingerprints otherwise not visible to the naked eye (called latents), provide the basis for automatic identification of latent fingerprints. The method is based on the assumption, that including detection of relevant structure of interest from latent fingerprint image into an effective segmentation model pipeline improves the effectiveness of the model and efficiency of the automated segmentation. The approach discards detections of poor-quality due to noise, inadequate data, misplaced structures of interests from multiple instances of fingermarks in the image etc. A collaborative detector-segmentation approach is proposed which establishes reproducibility and repeatability of the model, consequently increasing the efficiency of the frame of work. The results are obtained on IIIT -DCLF database. Performing saliency-based detection using color based visual distortion reducing the subsequent information processing cost through a stack of the convolutional autoencoder. The results obtained signify significant improvement over published results.
Author Ravulakollu, Kiran Kumar
Chhabra, Megha
Shukla, Manoj Kumar
Author_xml – sequence: 1
  givenname: Megha
  surname: Chhabra
  fullname: Chhabra, Megha
  email: lMegha.chhbr@gmail.com
  organization: AIIT, Amity University,Noida,UP,India
– sequence: 2
  givenname: Manoj Kumar
  surname: Shukla
  fullname: Shukla, Manoj Kumar
  email: mkshukla@amity.edu
  organization: ASET, Amity University,Noida,UP,India
– sequence: 3
  givenname: Kiran Kumar
  surname: Ravulakollu
  fullname: Ravulakollu, Kiran Kumar
  email: linekkravulakollu@ddn.upes.ac.in
  organization: School of Computer Science, University of Petroleum and Energy Studies Bidoli,Dehradun,India
BookMark eNotkN1OwjAUx6vRRESewMTsBYZtz9rSSzIBSZZoolyTbj3TKrS4FRLe3qJcnO__-eXk3JIrHzwS8sDomDGqH1-fFqWgwGDMKadjDUyoQl6QkVYTpngyBlxekgEXCnKgSt-QUd9_UUqBUyikHpCf5XbXhYPzH1n8xGzWtq5x6JtjFtpsuo9hayLarErex2yedNjtOpfypU0dl-QmuuCzVX9ivEXTfJ9Wy-APYbM_jczmD5QnarDY3ZHr1mx6HJ3jkKzms_fyOa9eFstyWuWOsUnMmWEoDee6aKG2FKTCosBa1BO0itWNFjWXFpRWTGhqBUUtuVYt2FRILWFI7v-5DhHX6eat6Y7r85PgF6scXuA
ContentType Conference Proceeding
DBID 6IE
6IL
CBEJK
RIE
RIL
DOI 10.1109/PDGC50313.2020.9315746
DatabaseName IEEE Electronic Library (IEL) Conference Proceedings
IEEE Xplore POP ALL
IEEE Xplore All Conference Proceedings
IEEE Electronic Library (IEL)
IEEE Proceedings Order Plans (POP All) 1998-Present
DatabaseTitleList
Database_xml – sequence: 1
  dbid: RIE
  name: IEEE Electronic Library (IEL)
  url: https://ieeexplore.ieee.org/
  sourceTypes: Publisher
DeliveryMethod fulltext_linktorsrc
Discipline Computer Science
EISBN 9781728171326
1728171326
EISSN 2573-3079
EndPage 196
ExternalDocumentID 9315746
Genre orig-research
GroupedDBID 6IE
6IF
6IL
6IN
AAJGR
AAWTH
ABLEC
ADZIZ
ALMA_UNASSIGNED_HOLDINGS
BEFXN
BFFAM
BGNUA
BKEBE
BPEOZ
CBEJK
CHZPO
IEGSK
OCL
RIE
RIL
ID FETCH-LOGICAL-i118t-1a1e6a2294f3bd0367e44eb5b8ed71bc95b26d37971590d50e96297f3d0d56963
IEDL.DBID RIE
IngestDate Wed Aug 27 06:03:58 EDT 2025
IsPeerReviewed false
IsScholarly false
Language English
LinkModel DirectLink
MergedId FETCHMERGED-LOGICAL-i118t-1a1e6a2294f3bd0367e44eb5b8ed71bc95b26d37971590d50e96297f3d0d56963
PageCount 6
ParticipantIDs ieee_primary_9315746
PublicationCentury 2000
PublicationDate 2020-Nov.-6
PublicationDateYYYYMMDD 2020-11-06
PublicationDate_xml – month: 11
  year: 2020
  text: 2020-Nov.-6
  day: 06
PublicationDecade 2020
PublicationTitle International Conference on Parallel, Distributed and Grid Computing (PDGC ...)
PublicationTitleAbbrev PDGC
PublicationYear 2020
Publisher IEEE
Publisher_xml – name: IEEE
SSID ssj0003203469
Score 1.7395754
Snippet In this paper, a method for improving the efficiency of latent fingerprint segmentation and detection system is presented. Structural detection and precise...
SourceID ieee
SourceType Publisher
StartPage 191
SubjectTerms Autoencoder Convolutional neural network
Classification
Computational modeling
Feature extraction
Fingerprint recognition
Image color analysis
Image processing
Image segmentation
Latent fingerprint
Load modeling
Segmentation
Training
Title Improving the Efficiency of Automated Latent Fingerprint Identification Using Stack of Convolutional Auto-encoder
URI https://ieeexplore.ieee.org/document/9315746
hasFullText 1
inHoldings 1
isFullTextHit
isPrint
link http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV09T8MwELXaioGpQIv4lgdG3MZ2bMcjKi0MVdUBpG6VY1-kCpRASfv7sd1QhMTCEiWRnER3se7u2e8dQrdGsoymAghliSJpLnNiFLUk08ZSEKkv5qK6_lTNZtlioectdLfnwgBA3HwGg3Aa1_JdZTcBKhtqToVKZRu1lZI7rtYeT-Es4b7Ua0jANNHD-cPjSARpQl8FsmTQDP7VRSUGkUn3f68_Qv0fNh6e7-PMMWpBeYK63-0YcDM7e-hjDxBgn9XhcRSHCMxKXBX4flNXPjkFh6f-WNZ4EvG8AOvVeMfWLRr4DsdtBNinofY1DPWfsW1-UPMWH0SC-qWDdR-9TMbPoyfSdFQgK19I1IQaCtIwptOC584HLwWpd4fIM3CK5laLnEnHlVY-y0mcSEBLplXBnb-Qfq6eok5ZlXCGsDUZJJzrDHSRQpIbro23P0ut1ZkT7Bz1ggWX7zvRjGVjvIu_b1-iw-CkSPKTV6hTrzdwjQ7stl59rm-ip78AOourKg
linkProvider IEEE
linkToHtml http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1NSwMxEA21CnqqWsVvc_DotvncbI5SrRVr6aFCbyWbzIIordZtf79JulYEL16W3YUsYWbDzLzkvUHoyqQso0JCQhlRicjTPDGK2iTTxlKQwhdzUV2_rwaDbDzWwxq6XnNhACAePoNWuI17-W5mFwEqa2tOpRLpBtqUQjCyYmutERXOCPfFXkUDpkS3h7f3HRnECX0dyEirGv6rj0oMI93G_yawiw5--Hh4uI40e6gG033U-G7IgKv12UQfa4gA-7wO30V5iMCtxLMC3yzKmU9PweG-v05L3I2IXgD2Srzi6xYVgIfjQQLsE1H7Gob6aSyrX9S8xQ8lQf_SwfwAPXfvRp1eUvVUSF58KVEm1FBIDWNaFDx3PnwpEN4hMs_AKZpbLXOWOq608nkOcZKATplWBXf-IfWr9RDVp7MpHCFsTQaEc52BLgSQ3HBtvP2ZsFZnTrJj1AwWnLyvZDMmlfFO_n59ibZ7o6f-pP8weDxFO8FhkfKXnqF6OV_AOdqyy_Llc34Rvf4FTPCucQ
openUrl ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&rft.genre=proceeding&rft.title=International+Conference+on+Parallel%2C+Distributed+and+Grid+Computing+%28PDGC+...%29&rft.atitle=Improving+the+Efficiency+of+Automated+Latent+Fingerprint+Identification+Using+Stack+of+Convolutional+Auto-encoder&rft.au=Chhabra%2C+Megha&rft.au=Shukla%2C+Manoj+Kumar&rft.au=Ravulakollu%2C+Kiran+Kumar&rft.date=2020-11-06&rft.pub=IEEE&rft.eissn=2573-3079&rft.spage=191&rft.epage=196&rft_id=info:doi/10.1109%2FPDGC50313.2020.9315746&rft.externalDocID=9315746