Biometry - Revision history

Henryk: Course of Action

2005-02-23T06:50:19Z

Course of Action

← Older revision		Revision as of 06:50, 23 February 2005
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	On the other hand some identification systems simply return the record that best matches the query. So when the person that is to be identified is not part of the database some —more or less— random record is returned. For that reason a common design for an identification system is to chain a '''indexing''' system which returns the <math>n</math> best matches and an authentication system.		On the other hand some identification systems simply return the record that best matches the query. So when the person that is to be identified is not part of the database some —more or less— random record is returned. For that reason a common design for an identification system is to chain a '''indexing''' system which returns the <math>n</math> best matches and an authentication system.

			==Course of Action==
			Being [[Wikipedia:Pattern recognition\|pattern recognition]] systems most biometric systems operate by first reducing the input (oftentimes an image) to a small set of features. Use of this set is twofold:
			# During the enrollment procedure, e.g. when the user is first introduced to the system, the set is transformed into a reference template which is then saved in a database. It is common to form the reference template by taking several measurements of the biometric and then averaging them.
			# During regular use in identification or authentication systems the set of features is compared against the reference template(s) from the database.

			<center>[[Image:Biometric_flow_small.png\|Typical biometric data flow for enrollment and authentication.]]<br>
			Typical flow of biometric data for the enrollment and authentication in an authentication system. There is also a [[:Image:Biometric_flow.png\|larger version]] available.</center>

	==Quality indices==		==Quality indices==

Henryk: Links

2005-02-22T06:18:10Z

Links

← Older revision		Revision as of 06:18, 22 February 2005
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	=Overview on Biometric Criteria=		=Overview on Biometric Criteria=
	There are several parts of the human anatomy that are sufficiently unique and stable during a long time and therefore suited for use as biometric criteria. The most common are:		There are several parts of the human anatomy that are sufficiently unique and stable during a long time and therefore suited for use as biometric criteria. The most common are:
	* fingerprints		* [[Fingerprint Matching\|fingerprints]]
	* faces		* [[Facial Recognition\|faces]]
	* hands		* [[Hand Geometry\|hands]]
	* irides		* [[Iris Scanning\|irides]]
	* retinae		* [[Retina Scanning\|retinae]]
	* vein patterns		* vein patterns

	There are also some personal behaviors or similar that can be used as biometrics:		There are also some personal behaviors or similar habits that can be used as biometrics:
	* signatures		* signatures
	* voices		* voices

Henryk: Overview on Biometric Criteria

2005-02-22T05:44:34Z

Overview on Biometric Criteria

← Older revision		Revision as of 05:44, 22 February 2005
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			=Introduction=
	'''Biometry''' is the statistical analysis of biological observations and phenomena([http://www.m-w.com/cgi-bin/dictionary?va=biometry]). Biometry is coupled with the measurement of '''biometric'''s: measurable, physical characteristics or personal behavioral traits used to recognize the identity, or verify the claimed identity of a person.		'''Biometry''' is the statistical analysis of biological observations and phenomena([http://www.m-w.com/cgi-bin/dictionary?va=biometry]). Biometry is coupled with the measurement of '''biometric'''s: measurable, physical characteristics or personal behavioral traits used to recognize the identity, or verify the claimed identity of a person.

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	Several quality indices are defined to compare different systems:		Several quality indices are defined to compare different systems:
	; False Rejection Rate (FRR) : This is the rate of authentication attempts from legitimate users that are rejected.		; False Rejection Rate (FRR) : This is the rate of authentication attempts from legitimate users that are rejected.
	; False Acceptance Rate (FAR) : This is the rate authentication attempts from illegitimate users (e.g. attackers) that are accepted.		; False Acceptance Rate (FAR) : This is the rate of authentication attempts from illegitimate users (e.g. attackers) that are accepted.
	; Equal Error Rate (EER) : This is the false acceptance rate (or the false rejection rate) when the system is set up so that the FRR equals the FAR.		; Equal Error Rate (EER) : This is the false acceptance rate (or the false rejection rate) when the system is set up so that the FRR equals the FAR.
	; Failure to Enroll Rate (FER) : This is the rate of users that can't enroll into the system at all.		; Failure to Enroll Rate (FER) : This is the rate of users that can't enroll into the system at all.
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	FRR and FAR curves and the point of EER.</center>		FRR and FAR curves and the point of EER.</center>

	A fundamental index in all systems that employ some sort of database of reference templates is the Failure to Enroll Rate, e.g. the rate of users that can't enroll into the system. In these systems a reference dataset must be aquired from each user prior to ~~all~~ operations (enrollment). This ~~is an~~ operation ~~that~~ can fail, for example due to intrinsic properties of the person trying to enroll. It would be impossible to enroll into a system based on hand geometries for a person that has no hands. Similarly a ~~dumb~~ person won't be able to enroll into a speech verification system, etc. And what use is a system with the best possible EER if two thirds of the prospective users can't enroll ~~into it~~?		A fundamental index in all systems that employ some sort of database of reference templates is the Failure to Enroll Rate, e.g. the rate of users that can't enroll into the system. In these systems a reference dataset must be aquired from each user prior to any operations (enrollment). This operation can fail, for example due to intrinsic properties of the person trying to enroll. E.g. it would be impossible to enroll into a system based on hand geometries for a person that has no hands. Similarly a mute person won't be able to enroll into a speech verification system, etc. And what use is a system with the best possible EER if two thirds of the prospective users can't enroll?

			=Overview on Biometric Criteria=
			There are several parts of the human anatomy that are sufficiently unique and stable during a long time and therefore suited for use as biometric criteria. The most common are:
			* fingerprints
			* faces
			* hands
			* irides
			* retinae
			* vein patterns

			There are also some personal behaviors or similar that can be used as biometrics:
			* signatures
			* voices
			* keystroke patterns

	=References=		=References=

Henryk: frr and far curves

2005-02-21T11:33:18Z

frr and far curves

← Older revision		Revision as of 11:33, 21 February 2005
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	'''Biometry''' is the statistical analysis of biological observations and phenomena([http://www.m-w.com/cgi-bin/dictionary?va=biometry]). Biometry is coupled with the measurement of '''biometric'''s: measurable, physical ~~characteristic~~ or personal behavioral traits used to recognize the identity, or verify the claimed identity of a person.		'''Biometry''' is the statistical analysis of biological observations and phenomena([http://www.m-w.com/cgi-bin/dictionary?va=biometry]). Biometry is coupled with the measurement of '''biometric'''s: measurable, physical characteristics or personal behavioral traits used to recognize the identity, or verify the claimed identity of a person.

	In the context of [[Security Engineering]] there are traditionally two links between a person and her identity:		In the context of [[Security Engineering]] there are traditionally two links between a person and her identity:
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	As different environments have different requirements regarding the security of the system and the convenience of the users most systems have some sort of adjustment possibility, e.g. a threshold value for the number of matches features. This can usually be set to trade off a higher FAR in favor of a lower FRR or vice versa. To aid in comparison of biometric systems one then uses the equal error rate: The adjustment is set so that FRR=FAR which then gives the EER. The EER of different systems can then be compared. (In reality mostly no one would use a system configured this way, as detailed above.)		As different environments have different requirements regarding the security of the system and the convenience of the users most systems have some sort of adjustment possibility, e.g. a threshold value for the number of matches features. This can usually be set to trade off a higher FAR in favor of a lower FRR or vice versa. To aid in comparison of biometric systems one then uses the equal error rate: The adjustment is set so that FRR=FAR which then gives the EER. The EER of different systems can then be compared. (In reality mostly no one would use a system configured this way, as detailed above.)

			<center>[[Image:Frrfar.png\|The graph shows some overly idealistic FRR and FAR curves depending on some sort of threshold and the EER point.]]<br>
			FRR and FAR curves and the point of EER.</center>

	A fundamental index in all systems that employ some sort of database of reference templates is the Failure to Enroll Rate, e.g. the rate of users that can't enroll into the system. In these systems a reference dataset must be aquired from each user prior to all operations (enrollment). This is an operation that can fail, for example due to intrinsic properties of the person trying to enroll. It would be impossible to enroll into a system based on hand geometries for a person that has no hands. Similarly a dumb person won't be able to enroll into a speech verification system, etc. And what use is a system with the best possible EER if two thirds of the prospective users can't enroll into it?		A fundamental index in all systems that employ some sort of database of reference templates is the Failure to Enroll Rate, e.g. the rate of users that can't enroll into the system. In these systems a reference dataset must be aquired from each user prior to all operations (enrollment). This is an operation that can fail, for example due to intrinsic properties of the person trying to enroll. It would be impossible to enroll into a system based on hand geometries for a person that has no hands. Similarly a dumb person won't be able to enroll into a speech verification system, etc. And what use is a system with the best possible EER if two thirds of the prospective users can't enroll into it?

Henryk: Quality indices

2005-02-21T11:21:52Z

Quality indices

← Older revision		Revision as of 11:21, 21 February 2005
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	==Modes of Operation==		==Modes of Operation==
	Two different modes must be distinguished:		Two different modes must be distinguished:
	;Identification: “Who is it?”		; Identification : “Who is it?”
	;Authentication: “Is it her?”		; Authentication : “Is it her?”

	Not all biometric systems can feasibly operate in both modes. For example if it takes a given system 1 second to authenticate a given user this would generally be deemed acceptable. If however the same system should be used to identify a user in a database of 10.000 users by simply trying the authentication on each database record in turn this would take 10.000 seconds or more than 2 hours and 46 minutes. In almost all but very few scenarios this will be completely unacceptable.		Not all biometric systems can feasibly operate in both modes. For example if it takes a given system 1 second to authenticate a given user this would generally be deemed acceptable. If however the same system should be used to identify a user in a database of 10.000 users by simply trying the authentication on each database record in turn this would take 10.000 seconds or more than 2 hours and 46 minutes. In almost all but very few scenarios this will be completely unacceptable.

	On the other hand some identification systems simply return the record that best matches the query. So when the person that is to be identified is not part of the database some —more or less— random record is returned. For that reason a common design for an identification system is to chain a '''indexing''' system which returns the <math>n</math> best matches and a authentication system.		On the other hand some identification systems simply return the record that best matches the query. So when the person that is to be identified is not part of the database some —more or less— random record is returned. For that reason a common design for an identification system is to chain a '''indexing''' system which returns the <math>n</math> best matches and an authentication system.

			==Quality indices==
			Several quality indices are defined to compare different systems:
			; False Rejection Rate (FRR) : This is the rate of authentication attempts from legitimate users that are rejected.
			; False Acceptance Rate (FAR) : This is the rate authentication attempts from illegitimate users (e.g. attackers) that are accepted.
			; Equal Error Rate (EER) : This is the false acceptance rate (or the false rejection rate) when the system is set up so that the FRR equals the FAR.
			; Failure to Enroll Rate (FER) : This is the rate of users that can't enroll into the system at all.
			Quite naturally the first three only apply to authentication systems.

			In most real world systems a high FAR is worse than a high FRR: When a legitimate user is rejected she simply tries again causing only a slight comfort loss. Accepting a illegitimate user, though, compromises the system and would usually lead to a loss of whatever the system was meant to protect.

			As different environments have different requirements regarding the security of the system and the convenience of the users most systems have some sort of adjustment possibility, e.g. a threshold value for the number of matches features. This can usually be set to trade off a higher FAR in favor of a lower FRR or vice versa. To aid in comparison of biometric systems one then uses the equal error rate: The adjustment is set so that FRR=FAR which then gives the EER. The EER of different systems can then be compared. (In reality mostly no one would use a system configured this way, as detailed above.)

			A fundamental index in all systems that employ some sort of database of reference templates is the Failure to Enroll Rate, e.g. the rate of users that can't enroll into the system. In these systems a reference dataset must be aquired from each user prior to all operations (enrollment). This is an operation that can fail, for example due to intrinsic properties of the person trying to enroll. It would be impossible to enroll into a system based on hand geometries for a person that has no hands. Similarly a dumb person won't be able to enroll into a speech verification system, etc. And what use is a system with the best possible EER if two thirds of the prospective users can't enroll into it?

	=References=		=References=

Henryk: modes of operation

2005-02-21T10:41:55Z

modes of operation

← Older revision		Revision as of 10:41, 21 February 2005
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	=Introduction=
	'''Biometry''' is the statistical analysis of biological observations and phenomena([http://www.m-w.com/cgi-bin/dictionary?va=biometry]). Biometry is coupled with the measurement of '''biometric'''s: measurable, physical characteristic or personal behavioral traits used to recognize the identity, or verify the claimed identity of a person.		'''Biometry''' is the statistical analysis of biological observations and phenomena([http://www.m-w.com/cgi-bin/dictionary?va=biometry]). Biometry is coupled with the measurement of '''biometric'''s: measurable, physical characteristic or personal behavioral traits used to recognize the identity, or verify the claimed identity of a person.

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	* Something I know ([[Passwords]])		* Something I know ([[Passwords]])
	* Something I have (tokens)		* Something I have (tokens)
	With biometry it should —in theory— possible to establish a third link:		With biometry it should —in theory— be possible to establish a third link:
	* Something I am		* Something I am

	Therefore biometric systems can play an important role in [[Wikipedia:Authentication\|authenticating]] a user.		Therefore biometric systems can play an important role in [[Wikipedia:Authentication\|authenticating]] a user.

			==Modes of Operation==
			Two different modes must be distinguished:
			;Identification: “Who is it?”
			;Authentication: “Is it her?”

			Not all biometric systems can feasibly operate in both modes. For example if it takes a given system 1 second to authenticate a given user this would generally be deemed acceptable. If however the same system should be used to identify a user in a database of 10.000 users by simply trying the authentication on each database record in turn this would take 10.000 seconds or more than 2 hours and 46 minutes. In almost all but very few scenarios this will be completely unacceptable.

			On the other hand some identification systems simply return the record that best matches the query. So when the person that is to be identified is not part of the database some —more or less— random record is returned. For that reason a common design for an identification system is to chain a '''indexing''' system which returns the <math>n</math> best matches and a authentication system.

	=References=		=References=

Henryk: Introduction

2005-02-21T10:21:56Z

Introduction

← Older revision		Revision as of 10:21, 21 February 2005
Line 1:		Line 1:
			=Introduction=
			'''Biometry''' is the statistical analysis of biological observations and phenomena([http://www.m-w.com/cgi-bin/dictionary?va=biometry]). Biometry is coupled with the measurement of '''biometric'''s: measurable, physical characteristic or personal behavioral traits used to recognize the identity, or verify the claimed identity of a person.

			In the context of [[Security Engineering]] there are traditionally two links between a person and her identity:
			* Something I know ([[Passwords]])
			* Something I have (tokens)
			With biometry it should —in theory— possible to establish a third link:
			* Something I am

			Therefore biometric systems can play an important role in [[Wikipedia:Authentication\|authenticating]] a user.

	=References=		=References=
	*Ashbourn, J. (2000). Biometrics: Advanced identity verification: The complete guide. London: Springer.		*Ashbourn, J. (2000). Biometrics: Advanced identity verification: The complete guide. London: Springer.

Henryk: Removed unnecessary copy of the slides, will add real content soon

2005-02-21T09:50:18Z

Removed unnecessary copy of the slides, will add real content soon

Show changes

141.20.21.175: /* References */

2005-02-15T13:38:04Z

References

← Older revision		Revision as of 13:38, 15 February 2005
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	=References=		=References=
	*Ashbourn, J. (2000). Biometrics: Advanced identity		*Ashbourn, J. (2000). Biometrics: Advanced identity verification: The complete guide. London: Springer.
			*Bhanu, B., & Tan, X. (2004). Computational algorithms for fingerprint recognition. Boston / Dordrecht/ London: Kluwer Academic Publishers.
	verification: The complete guide. London:
			*Thalheim, L., Krissler, J., & Ziegler, P.-M. (2002). Koerperkontrolle [Body check]. In c’t 11/2002. Hannover: Heise. (English version at http: //www.heise.de/ct/english/02/11/114/)
	Springer.
	*Bhanu, B., & Tan, X. (2004). Computational algorithms
	for fingerprint recognition. Boston / Dordrecht
	/ London: Kluwer Academic Publishers.
	*Thalheim, L., Krissler, J., & Ziegler, P.-M. (2002).
	Koerperkontrolle [Body check]. In c’t 11/2002.
	Hannover: Heise. (English version at http:
	//www.heise.de/ct/english/02/11/114/)

141.20.21.175: /* Problems */

2005-02-15T13:36:56Z

Problems

← Older revision		Revision as of 13:36, 15 February 2005
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	*are easy to fake on the most common sensor devices		*are easy to fake on the most common sensor devices
	*are usually not universally applicable (fer 5%)		*are usually not universally applicable (fer 5%)


			=References=
			*Ashbourn, J. (2000). Biometrics: Advanced identity
			verification: The complete guide. London:
			Springer.
			*Bhanu, B., & Tan, X. (2004). Computational algorithms
			for fingerprint recognition. Boston / Dordrecht
			/ London: Kluwer Academic Publishers.
			*Thalheim, L., Krissler, J., & Ziegler, P.-M. (2002).
			Koerperkontrolle [Body check]. In c’t 11/2002.
			Hannover: Heise. (English version at http:
			//www.heise.de/ct/english/02/11/114/)