Hand Veins Feature Extraction using DTCNNs

Suleyman Malki; Y Fuqiang; Lambert Spaanenburg

Hand Veins Feature Extraction using DTCNNs

Suleyman Malki, Y Fuqiang, Lambert Spaanenburg

Department of Electrical and Information Technology

Research output: Chapter in Book/Report/Conference proceeding › Paper in conference proceeding

Abstract

Biometric identification is an important security application that requires non-intrusive capture and real-time processing. Recently, identification by vein patterns has been suggested as a promising alternative. In this paper we study the potential of Cellular Neural Networks implemented on a Field-Programmable Gate Array to handle the person identification based on hand veins in real time. With a minimal distance measure of 2 pixels for False Feature Elimination, it has a True Acceptance Rate of 65% and a False Rejection Rate of 5%. The performance rises drastically with increasing pixel distance and will therefore be camera sensitive.

Original language	English
Title of host publication	Proceedings SSoCC
Publication status	Published - 2006
Event	SSoCC (Swedish System-on-Chip Conference), 2006 - Kolmården, Kålmården, Sweden Duration: 2006 May 4 → 2006 May 5

Conference

Conference	SSoCC (Swedish System-on-Chip Conference), 2006
Country/Territory	Sweden
City	Kålmården
Period	2006/05/04 → 2006/05/05

Subject classification (UKÄ)

Electrical Engineering, Electronic Engineering, Information Engineering

Cite this

@inproceedings{d05c7aff89174655b4d863d9b2e1b9db,

title = "Hand Veins Feature Extraction using DTCNNs",

abstract = "Biometric identification is an important security application that requires non-intrusive capture and real-time processing. Recently, identification by vein patterns has been suggested as a promising alternative. In this paper we study the potential of Cellular Neural Networks implemented on a Field-Programmable Gate Array to handle the person identification based on hand veins in real time. With a minimal distance measure of 2 pixels for False Feature Elimination, it has a True Acceptance Rate of 65\% and a False Rejection Rate of 5\%. The performance rises drastically with increasing pixel distance and will therefore be camera sensitive.",

author = "Suleyman Malki and Y Fuqiang and Lambert Spaanenburg",

year = "2006",

language = "English",

booktitle = "Proceedings SSoCC",

note = "SSoCC (Swedish System-on-Chip Conference), 2006 ; Conference date: 04-05-2006 Through 05-05-2006",

}

TY - GEN

T1 - Hand Veins Feature Extraction using DTCNNs

AU - Malki, Suleyman

AU - Fuqiang, Y

AU - Spaanenburg, Lambert

PY - 2006

Y1 - 2006

N2 - Biometric identification is an important security application that requires non-intrusive capture and real-time processing. Recently, identification by vein patterns has been suggested as a promising alternative. In this paper we study the potential of Cellular Neural Networks implemented on a Field-Programmable Gate Array to handle the person identification based on hand veins in real time. With a minimal distance measure of 2 pixels for False Feature Elimination, it has a True Acceptance Rate of 65% and a False Rejection Rate of 5%. The performance rises drastically with increasing pixel distance and will therefore be camera sensitive.

AB - Biometric identification is an important security application that requires non-intrusive capture and real-time processing. Recently, identification by vein patterns has been suggested as a promising alternative. In this paper we study the potential of Cellular Neural Networks implemented on a Field-Programmable Gate Array to handle the person identification based on hand veins in real time. With a minimal distance measure of 2 pixels for False Feature Elimination, it has a True Acceptance Rate of 65% and a False Rejection Rate of 5%. The performance rises drastically with increasing pixel distance and will therefore be camera sensitive.

M3 - Paper in conference proceeding

BT - Proceedings SSoCC

T2 - SSoCC (Swedish System-on-Chip Conference), 2006

Y2 - 4 May 2006 through 5 May 2006

ER -

Hand Veins Feature Extraction using DTCNNs

Abstract

Conference

Subject classification (UKÄ)

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Cite this