Registration of hand-grip pattern in smart gun

X. Shang; Raymond N.J. Veldhuis

Registration of hand-grip pattern in smart gun

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic

Abstract

Secure Grip1 focuses on the development of a hand-grip pattern recognition system, as part of a smart gun. It is intended for use by the police. We collected the hand-grip data from police officers in three sessions in a row with time intervals in between. If data for training and testing come from different sessions, however, the verification performance is not satisfactory. This is caused by the variations in data collected from different sessions of the same subject. Among others, one type of across-session variation is the hand shifts of subjects. Therefore we applied templatematching registration (TMR) as a data-preprocessing step. This has helped to reduce the Equal-Error Rate (EER) of the system by 1.7%. Besides, we discovered that the hand shifts of the same subject are on average smaller than those between different subjects. This has inspired the implementation of a new classifier, based on both the grip patterns and the hand shifts. With the new classifier the EER of the system has been further reduced by 3.0%.

Original language	Undefined
Title of host publication	17th Annual Workshop on Circuits
Place of Publication	Utrecht
Publisher	STW
Pages	192-195
Number of pages	4
ISBN (Print)	978-90-73461-44-4
Publication status	Published - 23 Nov 2006
Event	17th Annual Workshop on Circuits, Systems and Signal Processing, ProRISC 2006 - Veldhoven, Netherlands Duration: 23 Nov 2006 → 24 Nov 2006 Conference number: 17

Publication series

Name
Publisher	STW Technology Foundation
Number	Technical

Workshop

Workshop	17th Annual Workshop on Circuits, Systems and Signal Processing, ProRISC 2006
Abbreviated title	ProRISC 2006
Country	Netherlands
City	Veldhoven
Period	23/11/06 → 24/11/06

Keywords

SCS-Safety
METIS-237867
IR-63884
EWI-8918

Access to Document

http://eprints.eemcs.utwente.nl/secure2/8918/01/shang.pdf

Cite this

Shang, X., & Veldhuis, R. N. J. (2006). Registration of hand-grip pattern in smart gun. In 17th Annual Workshop on Circuits (pp. 192-195). Utrecht: STW.

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title = "Registration of hand-grip pattern in smart gun",

abstract = "Secure Grip1 focuses on the development of a hand-grip pattern recognition system, as part of a smart gun. It is intended for use by the police. We collected the hand-grip data from police officers in three sessions in a row with time intervals in between. If data for training and testing come from different sessions, however, the verification performance is not satisfactory. This is caused by the variations in data collected from different sessions of the same subject. Among others, one type of across-session variation is the hand shifts of subjects. Therefore we applied templatematching registration (TMR) as a data-preprocessing step. This has helped to reduce the Equal-Error Rate (EER) of the system by 1.7{\%}. Besides, we discovered that the hand shifts of the same subject are on average smaller than those between different subjects. This has inspired the implementation of a new classifier, based on both the grip patterns and the hand shifts. With the new classifier the EER of the system has been further reduced by 3.0{\%}.",

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AB - Secure Grip1 focuses on the development of a hand-grip pattern recognition system, as part of a smart gun. It is intended for use by the police. We collected the hand-grip data from police officers in three sessions in a row with time intervals in between. If data for training and testing come from different sessions, however, the verification performance is not satisfactory. This is caused by the variations in data collected from different sessions of the same subject. Among others, one type of across-session variation is the hand shifts of subjects. Therefore we applied templatematching registration (TMR) as a data-preprocessing step. This has helped to reduce the Equal-Error Rate (EER) of the system by 1.7%. Besides, we discovered that the hand shifts of the same subject are on average smaller than those between different subjects. This has inspired the implementation of a new classifier, based on both the grip patterns and the hand shifts. With the new classifier the EER of the system has been further reduced by 3.0%.

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