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Questioned document examination is extensively used by forensic specialists for criminal identification. This paper presents a writer recognition system based on allographic features operating in identification mode (one-to-many). It works at the level of isolated characters, considering that each writer uses a reduced number of shapes for each one. Individual characters of a writer are manually segmented and labeled by an expert as pertaining to one of 62 alphanumeric classes (10 numbers and 52 letters, including lowercase and uppercase letters), being the particular setup used by the forensic laboratory participating in this work. A codebook of shapes is then generated by clustering and the probability distribution function of allograph usage is the discriminative feature used for recognition. Results obtained on a database of 30 writers from real forensic documents show that the character class information given by the manual analysis provides a valuable source of improvement, justifying the proposed approach. We also evaluate the selection of different alphanumeric channels, showing a dependence between the size of the hit list and the number of channels needed for optimal performance. © 2010 IEEE.

The effect of different acquisition distances on the performance of face verification is studied. In particular, we evaluate two standard approaches using popular features (DCT and PCA) and matchers (GMM and SVM) under variation in the acquisition distance, as well as their score-level combination. The DCT-GMM-based system is found to be more robust to acquisition distance degradation than the PCASVM-based system. We exploit this fact by introducing an adaptive score fusion scheme based on a novel automatic scenario estimation which is shown to improve our system in uncontrolled environments. © 2010 IEEE.

The effect of different acquisition distances on the performance of face verification is studied. In particular, we evaluate two standard approaches using popular features (DCT and PCA) and matchers (GMM and SVM) under variation in the acquisition distance, as well as their score-level combination. The DCT-GMM-based system is found to be more robust to acquisition distance degradation than the PCASVM-based system. We exploit this fact by introducing an adaptive score fusion scheme based on a novel automatic scenario estimation which is shown to improve our system in uncontrolled environments. © 2010 IEEE.

Questioned document examination is extensively used by forensic specialists for criminal identification. This paper presents a writer recognition system based on allographic features operating in identification mode (one-to-many). It works at the level of isolated characters, considering that each writer uses a reduced number of shapes for each one. Individual characters of a writer are manually segmented and labeled by an expert as pertaining to one of 62 alphanumeric classes (10 numbers and 52 letters, including lowercase and uppercase letters), being the particular setup used by the forensic laboratory participating in this work. A codebook of shapes is then generated by clustering and the probability distribution function of allograph usage is the discriminative feature used for recognition. Results obtained on a database of 30 writers from real forensic documents show that the character class information given by the manual analysis provides a valuable source of improvement, justifying the proposed approach. We also evaluate the selection of different alphanumeric channels, showing a dependence between the size of the hit list and the number of channels needed for optimal performance. © 2010 IEEE.

The effect of different acquisition distances on the performance of face verification is studied. In particular, we evaluate two standard approaches using popular features (DCT and PCA) and matchers (GMM and SVM) under variation in the acquisition distance, as well as their score-level combination. The DCT-GMM-based system is found to be more robust to acquisition distance degradation than the PCASVM-based system. We exploit this fact by introducing an adaptive score fusion scheme based on a novel automatic scenario estimation which is shown to improve our system in uncontrolled environments. © 2010 IEEE.

Questioned document examination is extensively used by forensic specialists for criminal identification. This paper presents a writer recognition system based on allographic features operating in identification mode (one-to-many). It works at the level of isolated characters, considering that each writer uses a reduced number of shapes for each one. Individual characters of a writer are manually segmented and labeled by an expert as pertaining to one of 62 alphanumeric classes (10 numbers and 52 letters, including lowercase and uppercase letters), being the particular setup used by the forensic laboratory participating in this work. A codebook of shapes is then generated by clustering and the probability distribution function of allograph usage is the discriminative feature used for recognition. Results obtained on a database of 30 writers from real forensic documents show that the character class information given by the manual analysis provides a valuable source of improvement, justifying the proposed approach. We also evaluate the selection of different alphanumeric channels, showing a dependence between the size of the hit list and the number of channels needed for optimal performance. © 2010 IEEE.

Questioned document examination is extensively used by forensic specialists for criminal identification. This paper presents a writer recognition system based on allographic features operating in identification mode (one-to-many). It works at the level of isolated characters, considering that each writer uses a reduced number of shapes for each one. Individual characters of a writer are manually segmented and labeled by an expert as pertaining to one of 62 alphanumeric classes (10 numbers and 52 letters, including lowercase and uppercase letters), being the particular setup used by the forensic laboratory participating in this work. A codebook of shapes is then generated by clustering and the probability distribution function of allograph usage is the discriminative feature used for recognition. Results obtained on a database of 30 writers from real forensic documents show that the character class information given by the manual analysis provides a valuable source of improvement, justifying the proposed approach. We also evaluate the selection of different alphanumeric channels, showing a dependence between the size of the hit list and the number of channels needed for optimal performance. © 2010 IEEE.

The effect of different acquisition distances on the performance of face verification is studied. In particular, we evaluate two standard approaches using popular features (DCT and PCA) and matchers (GMM and SVM) under variation in the acquisition distance, as well as their score-level combination. The DCT-GMM-based system is found to be more robust to acquisition distance degradation than the PCASVM-based system. We exploit this fact by introducing an adaptive score fusion scheme based on a novel automatic scenario estimation which is shown to improve our system in uncontrolled environments. © 2010 IEEE.

Questioned document examination is extensively used by forensic specialists for criminal identification. This paper presents a writer recognition system based on allographic features operating in identification mode (one-to-many). It works at the level of isolated characters, considering that each writer uses a reduced number of shapes for each one. Individual characters of a writer are manually segmented and labeled by an expert as pertaining to one of 62 alphanumeric classes (10 numbers and 52 letters, including lowercase and uppercase letters), being the particular setup used by the forensic laboratory participating in this work. A codebook of shapes is then generated by clustering and the probability distribution function of allograph usage is the discriminative feature used for recognition. Results obtained on a database of 30 writers from real forensic documents show that the character class information given by the manual analysis provides a valuable source of improvement, justifying the proposed approach. We also evaluate the selection of different alphanumeric channels, showing a dependence between the size of the hit list and the number of channels needed for optimal performance. © 2010 IEEE.

The effect of different acquisition distances on the performance of face verification is studied. In particular, we evaluate two standard approaches using popular features (DCT and PCA) and matchers (GMM and SVM) under variation in the acquisition distance, as well as their score-level combination. The DCT-GMM-based system is found to be more robust to acquisition distance degradation than the PCASVM-based system. We exploit this fact by introducing an adaptive score fusion scheme based on a novel automatic scenario estimation which is shown to improve our system in uncontrolled environments. © 2010 IEEE.

The effect of different acquisition distances on the performance of face verification is studied. In particular, we evaluate two standard approaches using popular features (DCT and PCA) and matchers (GMM and SVM) under variation in the acquisition distance, as well as their score-level combination. The DCT-GMM-based system is found to be more robust to acquisition distance degradation than the PCASVM-based system. We exploit this fact by introducing an adaptive score fusion scheme based on a novel automatic scenario estimation which is shown to improve our system in uncontrolled environments. © 2010 IEEE.

Questioned document examination is extensively used by forensic specialists for criminal identification. This paper presents a writer recognition system based on allographic features operating in identification mode (one-to-many). It works at the level of isolated characters, considering that each writer uses a reduced number of shapes for each one. Individual characters of a writer are manually segmented and labeled by an expert as pertaining to one of 62 alphanumeric classes (10 numbers and 52 letters, including lowercase and uppercase letters), being the particular setup used by the forensic laboratory participating in this work. A codebook of shapes is then generated by clustering and the probability distribution function of allograph usage is the discriminative feature used for recognition. Results obtained on a database of 30 writers from real forensic documents show that the character class information given by the manual analysis provides a valuable source of improvement, justifying the proposed approach. We also evaluate the selection of different alphanumeric channels, showing a dependence between the size of the hit list and the number of channels needed for optimal performance. © 2010 IEEE.

In this chapter, we first provide an overview of the existing main approaches, databases, evaluation campaigns and the remaining challenges in online handwritten signature verification. We then propose a new benchmarking framework for online signature verification by introducing the concepts of “Reference Systems”, “Reference Databases” and associated “Reference Protocols.” Finally, we present the results of several approaches within the proposed evaluation framework. Among them are also present the best approaches within the first international Signature Verification Competition held in 2004 (SVC’2004), Dynamic Time Warping and Hidden Markov Models. All these systems are evaluated first within the benchmarking framework and also with other relevant protocols. Experiments are also reported on two different databases (BIOMET and MCYT) showing the impact of time variability for online signature verification. The two reference systems presented in this chapter are also used and evaluated in the BMEC’2007 evaluation campaign, presented in Chap11. ©Springer 2009

This paper evaluates the combination of static image (off-line) and dynamic information (on-line) for signature verification. Two off-line and two on-line recognition approaches exploiting information at the global and local levels are used. Experimental results are given using the BiosecurID database (130 signers, 3,640 signatures). Fusion experiments are done using a trained fusion approach based on linear logistic regression. It is shown experimentally that the local systems outperform the global ones, both in the on-line and in the off-line case. We also observe a considerable improvement when combining the two on-line systems, which is not the case with the off-line systems. The best performance is obtained when fusing all the systems together, which is specially evident for skilled forgeries when enough training data is available. ©2009 IEEE.

One of the biggest challenges in person recognition using biometric systems is the variability in the acquired data. In this paper, we evaluate the effects of an increasing time lapse between reference and test biometric data consisting of static images of handwritten signatures and texts. We use for our experiments two recognition approaches exploiting information at the global and local levels, and the BiosecurlD database, containing 3,724 signature images and 532 texts of 133 individuals acquired in four acquisition sessions distributed along a 4 months time span. We report results of the recognition systems working both in verification (one-to-one) and identification (one-to-many) mode. The results show the extent of the impact that the time separation between samples under comparison has on the recognition rates, being the local approach more robust to the time lapse than the global one. We also observe in our experiments that recognition based on handwritten texts provides higher accuracy than recognition based on signatures.

In this chapter, we first provide an overview of the existing main approaches, databases, evaluation campaigns and the remaining challenges in online handwritten signature verification. We then propose a new benchmarking framework for online signature verification by introducing the concepts of “Reference Systems”, “Reference Databases” and associated “Reference Protocols.” Finally, we present the results of several approaches within the proposed evaluation framework. Among them are also present the best approaches within the first international Signature Verification Competition held in 2004 (SVC’2004), Dynamic Time Warping and Hidden Markov Models. All these systems are evaluated first within the benchmarking framework and also with other relevant protocols. Experiments are also reported on two different databases (BIOMET and MCYT) showing the impact of time variability for online signature verification. The two reference systems presented in this chapter are also used and evaluated in the BMEC’2007 evaluation campaign, presented in Chap11. ©Springer 2009

This paper evaluates the combination of static image (off-line) and dynamic information (on-line) for signature verification. Two off-line and two on-line recognition approaches exploiting information at the global and local levels are used. Experimental results are given using the BiosecurID database (130 signers, 3,640 signatures). Fusion experiments are done using a trained fusion approach based on linear logistic regression. It is shown experimentally that the local systems outperform the global ones, both in the on-line and in the off-line case. We also observe a considerable improvement when combining the two on-line systems, which is not the case with the off-line systems. The best performance is obtained when fusing all the systems together, which is specially evident for skilled forgeries when enough training data is available. ©2009 IEEE.

One of the biggest challenges in person recognition using biometric systems is the variability in the acquired data. In this paper, we evaluate the effects of an increasing time lapse between reference and test biometric data consisting of static images of handwritten signatures and texts. We use for our experiments two recognition approaches exploiting information at the global and local levels, and the BiosecurlD database, containing 3,724 signature images and 532 texts of 133 individuals acquired in four acquisition sessions distributed along a 4 months time span. We report results of the recognition systems working both in verification (one-to-one) and identification (one-to-many) mode. The results show the extent of the impact that the time separation between samples under comparison has on the recognition rates, being the local approach more robust to the time lapse than the global one. We also observe in our experiments that recognition based on handwritten texts provides higher accuracy than recognition based on signatures.

In this chapter, we first provide an overview of the existing main approaches, databases, evaluation campaigns and the remaining challenges in online handwritten signature verification. We then propose a new benchmarking framework for online signature verification by introducing the concepts of “Reference Systems”, “Reference Databases” and associated “Reference Protocols.” Finally, we present the results of several approaches within the proposed evaluation framework. Among them are also present the best approaches within the first international Signature Verification Competition held in 2004 (SVC’2004), Dynamic Time Warping and Hidden Markov Models. All these systems are evaluated first within the benchmarking framework and also with other relevant protocols. Experiments are also reported on two different databases (BIOMET and MCYT) showing the impact of time variability for online signature verification. The two reference systems presented in this chapter are also used and evaluated in the BMEC’2007 evaluation campaign, presented in Chap11. ©Springer 2009

One of the biggest challenges in person recognition using biometric systems is the variability in the acquired data. In this paper, we evaluate the effects of an increasing time lapse between reference and test biometric data consisting of static images of handwritten signatures and texts. We use for our experiments two recognition approaches exploiting information at the global and local levels, and the BiosecurlD database, containing 3,724 signature images and 532 texts of 133 individuals acquired in four acquisition sessions distributed along a 4 months time span. We report results of the recognition systems working both in verification (one-to-one) and identification (one-to-many) mode. The results show the extent of the impact that the time separation between samples under comparison has on the recognition rates, being the local approach more robust to the time lapse than the global one. We also observe in our experiments that recognition based on handwritten texts provides higher accuracy than recognition based on signatures.

This paper evaluates the combination of static image (off-line) and dynamic information (on-line) for signature verification. Two off-line and two on-line recognition approaches exploiting information at the global and local levels are used. Experimental results are given using the BiosecurID database (130 signers, 3,640 signatures). Fusion experiments are done using a trained fusion approach based on linear logistic regression. It is shown experimentally that the local systems outperform the global ones, both in the on-line and in the off-line case. We also observe a considerable improvement when combining the two on-line systems, which is not the case with the off-line systems. The best performance is obtained when fusing all the systems together, which is specially evident for skilled forgeries when enough training data is available. ©2009 IEEE.

In this chapter, we first provide an overview of the existing main approaches, databases, evaluation campaigns and the remaining challenges in online handwritten signature verification. We then propose a new benchmarking framework for online signature verification by introducing the concepts of “Reference Systems”, “Reference Databases” and associated “Reference Protocols.” Finally, we present the results of several approaches within the proposed evaluation framework. Among them are also present the best approaches within the first international Signature Verification Competition held in 2004 (SVC’2004), Dynamic Time Warping and Hidden Markov Models. All these systems are evaluated first within the benchmarking framework and also with other relevant protocols. Experiments are also reported on two different databases (BIOMET and MCYT) showing the impact of time variability for online signature verification. The two reference systems presented in this chapter are also used and evaluated in the BMEC’2007 evaluation campaign, presented in Chap11. ©Springer 2009

This paper evaluates the combination of static image (off-line) and dynamic information (on-line) for signature verification. Two off-line and two on-line recognition approaches exploiting information at the global and local levels are used. Experimental results are given using the BiosecurID database (130 signers, 3,640 signatures). Fusion experiments are done using a trained fusion approach based on linear logistic regression. It is shown experimentally that the local systems outperform the global ones, both in the on-line and in the off-line case. We also observe a considerable improvement when combining the two on-line systems, which is not the case with the off-line systems. The best performance is obtained when fusing all the systems together, which is specially evident for skilled forgeries when enough training data is available. ©2009 IEEE.

In this chapter, we first provide an overview of the existing main approaches, databases, evaluation campaigns and the remaining challenges in online handwritten signature verification. We then propose a new benchmarking framework for online signature verification by introducing the concepts of “Reference Systems”, “Reference Databases” and associated “Reference Protocols.” Finally, we present the results of several approaches within the proposed evaluation framework. Among them are also present the best approaches within the first international Signature Verification Competition held in 2004 (SVC’2004), Dynamic Time Warping and Hidden Markov Models. All these systems are evaluated first within the benchmarking framework and also with other relevant protocols. Experiments are also reported on two different databases (BIOMET and MCYT) showing the impact of time variability for online signature verification. The two reference systems presented in this chapter are also used and evaluated in the BMEC’2007 evaluation campaign, presented in Chap11. ©Springer 2009

One of the biggest challenges in person recognition using biometric systems is the variability in the acquired data. In this paper, we evaluate the effects of an increasing time lapse between reference and test biometric data consisting of static images of handwritten signatures and texts. We use for our experiments two recognition approaches exploiting information at the global and local levels, and the BiosecurlD database, containing 3,724 signature images and 532 texts of 133 individuals acquired in four acquisition sessions distributed along a 4 months time span. We report results of the recognition systems working both in verification (one-to-one) and identification (one-to-many) mode. The results show the extent of the impact that the time separation between samples under comparison has on the recognition rates, being the local approach more robust to the time lapse than the global one. We also observe in our experiments that recognition based on handwritten texts provides higher accuracy than recognition based on signatures.

One of the biggest challenges in person recognition using biometric systems is the variability in the acquired data. In this paper, we evaluate the effects of an increasing time lapse between reference and test biometric data consisting of static images of handwritten signatures and texts. We use for our experiments two recognition approaches exploiting information at the global and local levels, and the BiosecurlD database, containing 3,724 signature images and 532 texts of 133 individuals acquired in four acquisition sessions distributed along a 4 months time span. We report results of the recognition systems working both in verification (one-to-one) and identification (one-to-many) mode. The results show the extent of the impact that the time separation between samples under comparison has on the recognition rates, being the local approach more robust to the time lapse than the global one. We also observe in our experiments that recognition based on handwritten texts provides higher accuracy than recognition based on signatures.

This paper evaluates the combination of static image (off-line) and dynamic information (on-line) for signature verification. Two off-line and two on-line recognition approaches exploiting information at the global and local levels are used. Experimental results are given using the BiosecurID database (130 signers, 3,640 signatures). Fusion experiments are done using a trained fusion approach based on linear logistic regression. It is shown experimentally that the local systems outperform the global ones, both in the on-line and in the off-line case. We also observe a considerable improvement when combining the two on-line systems, which is not the case with the off-line systems. The best performance is obtained when fusing all the systems together, which is specially evident for skilled forgeries when enough training data is available. ©2009 IEEE.

In this chapter, we first provide an overview of the existing main approaches, databases, evaluation campaigns and the remaining challenges in online handwritten signature verification. We then propose a new benchmarking framework for online signature verification by introducing the concepts of “Reference Systems”, “Reference Databases” and associated “Reference Protocols.” Finally, we present the results of several approaches within the proposed evaluation framework. Among them are also present the best approaches within the first international Signature Verification Competition held in 2004 (SVC’2004), Dynamic Time Warping and Hidden Markov Models. All these systems are evaluated first within the benchmarking framework and also with other relevant protocols. Experiments are also reported on two different databases (BIOMET and MCYT) showing the impact of time variability for online signature verification. The two reference systems presented in this chapter are also used and evaluated in the BMEC’2007 evaluation campaign, presented in Chap11. ©Springer 2009

One of the biggest challenges in person recognition using biometric systems is the variability in the acquired data. In this paper, we evaluate the effects of an increasing time lapse between reference and test biometric data consisting of static images of handwritten signatures and texts. We use for our experiments two recognition approaches exploiting information at the global and local levels, and the BiosecurlD database, containing 3,724 signature images and 532 texts of 133 individuals acquired in four acquisition sessions distributed along a 4 months time span. We report results of the recognition systems working both in verification (one-to-one) and identification (one-to-many) mode. The results show the extent of the impact that the time separation between samples under comparison has on the recognition rates, being the local approach more robust to the time lapse than the global one. We also observe in our experiments that recognition based on handwritten texts provides higher accuracy than recognition based on signatures.

This paper evaluates the combination of static image (off-line) and dynamic information (on-line) for signature verification. Two off-line and two on-line recognition approaches exploiting information at the global and local levels are used. Experimental results are given using the BiosecurID database (130 signers, 3,640 signatures). Fusion experiments are done using a trained fusion approach based on linear logistic regression. It is shown experimentally that the local systems outperform the global ones, both in the on-line and in the off-line case. We also observe a considerable improvement when combining the two on-line systems, which is not the case with the off-line systems. The best performance is obtained when fusing all the systems together, which is specially evident for skilled forgeries when enough training data is available. ©2009 IEEE.

In this chapter, we first provide an overview of the existing main approaches, databases, evaluation campaigns and the remaining challenges in online handwritten signature verification. We then propose a new benchmarking framework for online signature verification by introducing the concepts of “Reference Systems”, “Reference Databases” and associated “Reference Protocols.” Finally, we present the results of several approaches within the proposed evaluation framework. Among them are also present the best approaches within the first international Signature Verification Competition held in 2004 (SVC’2004), Dynamic Time Warping and Hidden Markov Models. All these systems are evaluated first within the benchmarking framework and also with other relevant protocols. Experiments are also reported on two different databases (BIOMET and MCYT) showing the impact of time variability for online signature verification. The two reference systems presented in this chapter are also used and evaluated in the BMEC’2007 evaluation campaign, presented in Chap11. ©Springer 2009

In this contribution, the vulnerabilities of iris-based recognition systems to direct attacks are studied. A database of fake iris images has been created from real iris of the BioSec baseline database. Iris images are printed using a commercial printer and then, presented at the iris sensor. We use for our experiments a publicly available iris recognition system, which some modifications to improve the iris segmentation step. Based on results achieved on different operational scenarios, we show that the system is vulnerable to direct attacks, pointing out the importance of having countermeasures against this type of fraudulent actions. © 2008 Springer Berlin Heidelberg.

An off-line signature verification system based on contour features is presented. It works at the local image level, and encodes directional properties of signature contours and the length of regions enclosed inside letters. Results obtained on a sub-corpus of the MCYT signature database shows that directional-based features work much better than length-based features. Results are comparable to existing approaches based on different features. It is also observed that combination of the proposed features does not provide improvements in performance, maybe to some existing correlation among them.

In this paper, we evaluate the effects of time separation between acquisitions in iris recognition. We use for our experiments a publicly available iris recognition system and the BiosecurlD database, containing 8128 iris images of 254 individuals acquired in four acquisition sessions, separated by one to four weeks between consecutive sessions. Reported results show that time separation between iris samples under comparison has severe impact on the recognition rates. An important degradation on the False Rejection Rate is observed, meaning that the intra-class variability is increased. All images in our database have been acquired under similar controlled conditions and with the same sensor. © 2008 IEEE.

Multimodal biometric systems allow to overcome some of the problems presented in unimodal systems, such as non-universality, lack of distinctiveness of the unimodal trait, noise in the acquired data, etc. Integration at the matching score level is the most common approach used due to the ease in combining the scores generated by different unimodal systems. Unfortunately, scores usually lie in application-dependent domains. In this work, we use linear logistic regression fusion, in which fused scores tend to be calibrated log-likelihood-ratios and thus, independent of the application. We use for our experiments the development set of scores of the DS2 Evaluation (Access Control Scenario) of the BioSecure Multimodal Evaluation Campaign, whose objective is to compare the performance of fusion algorithms when query biometric signals are originated from heterogeneous biometric devices. We compare a fusion scheme that uses linear logistic regression with a set of simple fusion rules. It is observed that the proposed fusion scheme outperforms all the simple fusion rules, with the additional advantage of the application-independent nature of the resulting fused scores.

Multimodal biometric systems allow to overcome some of the problems presented in unimodal systems, such as non-universality, lack of distinctiveness of the unimodal trait, noise in the acquired data, etc. Integration at the matching score level is the most common approach used due to the ease in combining the scores generated by different unimodal systems. Unfortunately, scores usually lie in application-dependent domains. In this work, we use linear logistic regression fusion, in which fused scores tend to be calibrated log-likelihood-ratios and thus, independent of the application. We use for our experiments the development set of scores of the DS2 Evaluation (Access Control Scenario) of the BioSecure Multimodal Evaluation Campaign, whose objective is to compare the performance of fusion algorithms when query biometric signals are originated from heterogeneous biometric devices. We compare a fusion scheme that uses linear logistic regression with a set of simple fusion rules. It is observed that the proposed fusion scheme outperforms all the simple fusion rules, with the additional advantage of the application-independent nature of the resulting fused scores.

An off-line signature verification system based on contour features is presented. It works at the local image level, and encodes directional properties of signature contours and the length of regions enclosed inside letters. Results obtained on a sub-corpus of the MCYT signature database shows that directional-based features work much better than length-based features. Results are comparable to existing approaches based on different features. It is also observed that combination of the proposed features does not provide improvements in performance, maybe to some existing correlation among them.

In this contribution, the vulnerabilities of iris-based recognition systems to direct attacks are studied. A database of fake iris images has been created from real iris of the BioSec baseline database. Iris images are printed using a commercial printer and then, presented at the iris sensor. We use for our experiments a publicly available iris recognition system, which some modifications to improve the iris segmentation step. Based on results achieved on different operational scenarios, we show that the system is vulnerable to direct attacks, pointing out the importance of having countermeasures against this type of fraudulent actions. © 2008 Springer Berlin Heidelberg.

In this paper, we evaluate the effects of time separation between acquisitions in iris recognition. We use for our experiments a publicly available iris recognition system and the BiosecurlD database, containing 8128 iris images of 254 individuals acquired in four acquisition sessions, separated by one to four weeks between consecutive sessions. Reported results show that time separation between iris samples under comparison has severe impact on the recognition rates. An important degradation on the False Rejection Rate is observed, meaning that the intra-class variability is increased. All images in our database have been acquired under similar controlled conditions and with the same sensor. © 2008 IEEE.

Multimodal biometric systems allow to overcome some of the problems presented in unimodal systems, such as non-universality, lack of distinctiveness of the unimodal trait, noise in the acquired data, etc. Integration at the matching score level is the most common approach used due to the ease in combining the scores generated by different unimodal systems. Unfortunately, scores usually lie in application-dependent domains. In this work, we use linear logistic regression fusion, in which fused scores tend to be calibrated log-likelihood-ratios and thus, independent of the application. We use for our experiments the development set of scores of the DS2 Evaluation (Access Control Scenario) of the BioSecure Multimodal Evaluation Campaign, whose objective is to compare the performance of fusion algorithms when query biometric signals are originated from heterogeneous biometric devices. We compare a fusion scheme that uses linear logistic regression with a set of simple fusion rules. It is observed that the proposed fusion scheme outperforms all the simple fusion rules, with the additional advantage of the application-independent nature of the resulting fused scores.

In this contribution, the vulnerabilities of iris-based recognition systems to direct attacks are studied. A database of fake iris images has been created from real iris of the BioSec baseline database. Iris images are printed using a commercial printer and then, presented at the iris sensor. We use for our experiments a publicly available iris recognition system, which some modifications to improve the iris segmentation step. Based on results achieved on different operational scenarios, we show that the system is vulnerable to direct attacks, pointing out the importance of having countermeasures against this type of fraudulent actions. © 2008 Springer Berlin Heidelberg.

An off-line signature verification system based on contour features is presented. It works at the local image level, and encodes directional properties of signature contours and the length of regions enclosed inside letters. Results obtained on a sub-corpus of the MCYT signature database shows that directional-based features work much better than length-based features. Results are comparable to existing approaches based on different features. It is also observed that combination of the proposed features does not provide improvements in performance, maybe to some existing correlation among them.

In this paper, we evaluate the effects of time separation between acquisitions in iris recognition. We use for our experiments a publicly available iris recognition system and the BiosecurlD database, containing 8128 iris images of 254 individuals acquired in four acquisition sessions, separated by one to four weeks between consecutive sessions. Reported results show that time separation between iris samples under comparison has severe impact on the recognition rates. An important degradation on the False Rejection Rate is observed, meaning that the intra-class variability is increased. All images in our database have been acquired under similar controlled conditions and with the same sensor. © 2008 IEEE.

An off-line signature verification system based on contour features is presented. It works at the local image level, and encodes directional properties of signature contours and the length of regions enclosed inside letters. Results obtained on a sub-corpus of the MCYT signature database shows that directional-based features work much better than length-based features. Results are comparable to existing approaches based on different features. It is also observed that combination of the proposed features does not provide improvements in performance, maybe to some existing correlation among them.

In this paper, we evaluate the effects of time separation between acquisitions in iris recognition. We use for our experiments a publicly available iris recognition system and the BiosecurlD database, containing 8128 iris images of 254 individuals acquired in four acquisition sessions, separated by one to four weeks between consecutive sessions. Reported results show that time separation between iris samples under comparison has severe impact on the recognition rates. An important degradation on the False Rejection Rate is observed, meaning that the intra-class variability is increased. All images in our database have been acquired under similar controlled conditions and with the same sensor. © 2008 IEEE.

In this contribution, the vulnerabilities of iris-based recognition systems to direct attacks are studied. A database of fake iris images has been created from real iris of the BioSec baseline database. Iris images are printed using a commercial printer and then, presented at the iris sensor. We use for our experiments a publicly available iris recognition system, which some modifications to improve the iris segmentation step. Based on results achieved on different operational scenarios, we show that the system is vulnerable to direct attacks, pointing out the importance of having countermeasures against this type of fraudulent actions. © 2008 Springer Berlin Heidelberg.