Pub Date : 2015-03-23DOI: 10.1109/ISBA.2015.7126360
Hossein Talebi, M. Gavrilova
Multimodal biometric systems use multiple biometrics traits to increase the recognition rate. The fusion module plays a key role in multi-biometric system performance. This paper presents a novel multimodal rank reinforcement approach based on the prior resemblance probability distribution of each identity in the training data. The resemblance probability distribution is used before the fusion to reinforce the rank list of each biometric matcher. In this paper, we developed a multimodal biometric system based on the frontal face, the profiles face, and the ear. The experimental results show the ability of the prior reinforcement in increasing the accuracy of unimodal biometrics systems as well as increasing the recognition rate of various rank level fusion approaches.
{"title":"Prior resemblance probability of users for multimodal biometrics rank fusion","authors":"Hossein Talebi, M. Gavrilova","doi":"10.1109/ISBA.2015.7126360","DOIUrl":"https://doi.org/10.1109/ISBA.2015.7126360","url":null,"abstract":"Multimodal biometric systems use multiple biometrics traits to increase the recognition rate. The fusion module plays a key role in multi-biometric system performance. This paper presents a novel multimodal rank reinforcement approach based on the prior resemblance probability distribution of each identity in the training data. The resemblance probability distribution is used before the fusion to reinforce the rank list of each biometric matcher. In this paper, we developed a multimodal biometric system based on the frontal face, the profiles face, and the ear. The experimental results show the ability of the prior reinforcement in increasing the accuracy of unimodal biometrics systems as well as increasing the recognition rate of various rank level fusion approaches.","PeriodicalId":398910,"journal":{"name":"IEEE International Conference on Identity, Security and Behavior Analysis (ISBA 2015)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123721410","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2015-03-23DOI: 10.1109/ISBA.2015.7126346
Guoqiang Li, Bian Yang, C. Busch
Biometrics identification systems containing a largescale database have been gaining increasing attention. In order to speed up searching in a large-scale fingerprint database, fingerprint indexing algorithm has been studied and introduced into biometrics identification system. One critical component of a fingerprint indexing algorithm is the feature extraction method. Majority of researchers developed the features by combining minutia with other information, such as ridge, singularities, orientation filed, etc. Instead, this paper will focus on only using minutia location and direction to extract features. The performance of proposed fingerprint indexing approach was evaluated on several public databases by being compared to the start-of-the- art fingerprint indexing method - minutia cylinder-code (MCC) - indexing as a benchmark. The experimental results show that the proposed approach gives equivalent performance or even outperforms MCC indexing method on the tested databases.
{"title":"A Novel Fingerprint Indexing Approach Focusing on Minutia Location and direction","authors":"Guoqiang Li, Bian Yang, C. Busch","doi":"10.1109/ISBA.2015.7126346","DOIUrl":"https://doi.org/10.1109/ISBA.2015.7126346","url":null,"abstract":"Biometrics identification systems containing a largescale database have been gaining increasing attention. In order to speed up searching in a large-scale fingerprint database, fingerprint indexing algorithm has been studied and introduced into biometrics identification system. One critical component of a fingerprint indexing algorithm is the feature extraction method. Majority of researchers developed the features by combining minutia with other information, such as ridge, singularities, orientation filed, etc. Instead, this paper will focus on only using minutia location and direction to extract features. The performance of proposed fingerprint indexing approach was evaluated on several public databases by being compared to the start-of-the- art fingerprint indexing method - minutia cylinder-code (MCC) - indexing as a benchmark. The experimental results show that the proposed approach gives equivalent performance or even outperforms MCC indexing method on the tested databases.","PeriodicalId":398910,"journal":{"name":"IEEE International Conference on Identity, Security and Behavior Analysis (ISBA 2015)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126826382","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2015-03-23DOI: 10.1109/ISBA.2015.7126351
Bin Li, Zifei Yan, W. Zuo, Feng Yue
Iris recognition is one of the most accurate biometric technologies. The uniqueness of iris, also known as iris individuality, has been widely accepted as one foundation for iris recognition. Although a few iris individuality models have been proposed, they are either incomplete or less accurate. In this paper, we investigate the iris individuality problem using Daugman's iris code method. We divide the bits in an iriscode into two groups, i.e., consistent and inconsistent bits, and provide the individuality analysis by both FAR and FRR modeling. Numeric evaluation using real iris data shows its usefulness in predicting the empirical performance. Furthermore, till now it is just experimentally confirmed that the recognition accuracy could be improved by masking out inconsistent bits. In order to formally e- valuate the effectiveness of this strategy, we derive the iris individuality model after masking out the inconsistent bits. Comparison of the two models has demonstrated the improved accuracy of the masking strategy, and the drop of EER is up to about 80%.
{"title":"Modeling the individuality of iris pattern and the effectiveness of inconsistent bit masking strategy","authors":"Bin Li, Zifei Yan, W. Zuo, Feng Yue","doi":"10.1109/ISBA.2015.7126351","DOIUrl":"https://doi.org/10.1109/ISBA.2015.7126351","url":null,"abstract":"Iris recognition is one of the most accurate biometric technologies. The uniqueness of iris, also known as iris individuality, has been widely accepted as one foundation for iris recognition. Although a few iris individuality models have been proposed, they are either incomplete or less accurate. In this paper, we investigate the iris individuality problem using Daugman's iris code method. We divide the bits in an iriscode into two groups, i.e., consistent and inconsistent bits, and provide the individuality analysis by both FAR and FRR modeling. Numeric evaluation using real iris data shows its usefulness in predicting the empirical performance. Furthermore, till now it is just experimentally confirmed that the recognition accuracy could be improved by masking out inconsistent bits. In order to formally e- valuate the effectiveness of this strategy, we derive the iris individuality model after masking out the inconsistent bits. Comparison of the two models has demonstrated the improved accuracy of the masking strategy, and the drop of EER is up to about 80%.","PeriodicalId":398910,"journal":{"name":"IEEE International Conference on Identity, Security and Behavior Analysis (ISBA 2015)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130290720","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2015-03-23DOI: 10.1109/ISBA.2015.7126362
S. Elliott, Kevin O'Connor, Eric Bartlow, Joshua J. Robertson, R. Guest
Biometric technologies represent a significant component of comprehensive digital identity solutions, and play an important role in crucial security tasks. These technologies support identification and authentication of individuals based on their physiological and behavioral characteristics. This has led many governmental agencies to choose biometrics as a supplement to existing identification schemes, most prominently ID cards and passports. Studies have shown that the success of biometric systems relies, in part, on how humans interact and accept such systems. In this paper, the authors build on previous work related to the Human-Biometric Sensor Interaction (HBSI) model and examine it with respect to the introduction of a token (e.g. an electronic passport or identity card) into the biometric system. The role of the imposter within an Identity Claim scenario has been integrated to expand the HBSI model into a full version, which is able to categorise potential False Claims and Attack Presentations.
{"title":"Expanding the human-biometric sensor interaction model to identity claim scenarios","authors":"S. Elliott, Kevin O'Connor, Eric Bartlow, Joshua J. Robertson, R. Guest","doi":"10.1109/ISBA.2015.7126362","DOIUrl":"https://doi.org/10.1109/ISBA.2015.7126362","url":null,"abstract":"Biometric technologies represent a significant component of comprehensive digital identity solutions, and play an important role in crucial security tasks. These technologies support identification and authentication of individuals based on their physiological and behavioral characteristics. This has led many governmental agencies to choose biometrics as a supplement to existing identification schemes, most prominently ID cards and passports. Studies have shown that the success of biometric systems relies, in part, on how humans interact and accept such systems. In this paper, the authors build on previous work related to the Human-Biometric Sensor Interaction (HBSI) model and examine it with respect to the introduction of a token (e.g. an electronic passport or identity card) into the biometric system. The role of the imposter within an Identity Claim scenario has been integrated to expand the HBSI model into a full version, which is able to categorise potential False Claims and Attack Presentations.","PeriodicalId":398910,"journal":{"name":"IEEE International Conference on Identity, Security and Behavior Analysis (ISBA 2015)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124987555","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2015-03-23DOI: 10.1109/ISBA.2015.7126358
J. B. Fernando, Koji Morikawa
In this paper, a novel method of human identification using electrocardiogram (ECG) is proposed. In the method, while normalizing RR interval, in addition to normalized signal where time interval of P wave, Q wave, R wave, S wave relatively to R wave is unaligned, normalized signal where time interval of those peaks is aligned is also generated. Wavelet transform is then applied to both normalized signals and feature vector is extracted from their wavelet coefficients. ECG data are collected from 10 subjects using a pair of dry electrodes which are held by two fingers. Experiment results show that adding wavelet of peak-aligned ECG improves the classification accuracy, where the maximum accuracy is 100%, 97%, and 90% for data measured in more than 20 seconds, 5 seconds, and 3 seconds respectively.
{"title":"Improvement of human identification accuracy by wavelet of peak-aligned ECG","authors":"J. B. Fernando, Koji Morikawa","doi":"10.1109/ISBA.2015.7126358","DOIUrl":"https://doi.org/10.1109/ISBA.2015.7126358","url":null,"abstract":"In this paper, a novel method of human identification using electrocardiogram (ECG) is proposed. In the method, while normalizing RR interval, in addition to normalized signal where time interval of P wave, Q wave, R wave, S wave relatively to R wave is unaligned, normalized signal where time interval of those peaks is aligned is also generated. Wavelet transform is then applied to both normalized signals and feature vector is extracted from their wavelet coefficients. ECG data are collected from 10 subjects using a pair of dry electrodes which are held by two fingers. Experiment results show that adding wavelet of peak-aligned ECG improves the classification accuracy, where the maximum accuracy is 100%, 97%, and 90% for data measured in more than 20 seconds, 5 seconds, and 3 seconds respectively.","PeriodicalId":398910,"journal":{"name":"IEEE International Conference on Identity, Security and Behavior Analysis (ISBA 2015)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116504251","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2015-03-23DOI: 10.1109/ISBA.2015.7126342
Soumik Mondal, Patrick A. H. Bours
In this research, we focus on context independent continuous authentication that reacts on every separate action performed by a user. The experimental data was collected in a complete uncontrolled condition from 53 users by using our data collection software. In our analysis, we considered both keystroke and mouse usage behaviour patterns to prevent a situation where an attacker avoids detection by restricting to one input device because the continuous authentication system only checks the other input device. The best result obtained from this research is that for 47 bio-metric subjects we have on average 275 actions required to detect an imposter where these biometric subjects are never locked out from the system.
{"title":"Context independent continuous authentication using behavioural biometrics","authors":"Soumik Mondal, Patrick A. H. Bours","doi":"10.1109/ISBA.2015.7126342","DOIUrl":"https://doi.org/10.1109/ISBA.2015.7126342","url":null,"abstract":"In this research, we focus on context independent continuous authentication that reacts on every separate action performed by a user. The experimental data was collected in a complete uncontrolled condition from 53 users by using our data collection software. In our analysis, we considered both keystroke and mouse usage behaviour patterns to prevent a situation where an attacker avoids detection by restricting to one input device because the continuous authentication system only checks the other input device. The best result obtained from this research is that for 47 bio-metric subjects we have on average 275 actions required to detect an imposter where these biometric subjects are never locked out from the system.","PeriodicalId":398910,"journal":{"name":"IEEE International Conference on Identity, Security and Behavior Analysis (ISBA 2015)","volume":"179 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121704038","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2015-03-23DOI: 10.1109/ISBA.2015.7126344
Daniel F. Smith, A. Wiliem, B. Lovell
Mobile devices (laptops, tablets, and smart phones) are ideal for the wide deployment of biometric authentication, such as face recognition. However, their uncontrolled use and distributed management increases the risk of remote compromise of the device by intruders or malicious programs. Such compromises may result in the device being used to capture the user's face image and replay it to gain unauthorized access to their online accounts, possibly from a different device. Replay attacks can be highly automated and are cheap to launch worldwide, as opposed to spoofing attacks which are relatively expensive as they must be tailored to each individual victim. In this paper, we propose a technique to address replay attacks for a face recognition system by embedding a binary watermark into the captured video. Our monochrome watermark provides high contrast between the signal states, resulting in a robust signal that is practical in a wide variety of environmental conditions. It is also robust to different cameras and tolerates relative movements well. In this paper, the proposed technique is validated on different subjects using several cameras in a variety of lighting conditions. In addition, we explore the limitations of current devices and environments that can negatively impact on performance, and propose solutions to reduce the impact of these limitations.
{"title":"Binary watermarks: a practical method to address face recognition replay attacks on consumer mobile devices","authors":"Daniel F. Smith, A. Wiliem, B. Lovell","doi":"10.1109/ISBA.2015.7126344","DOIUrl":"https://doi.org/10.1109/ISBA.2015.7126344","url":null,"abstract":"Mobile devices (laptops, tablets, and smart phones) are ideal for the wide deployment of biometric authentication, such as face recognition. However, their uncontrolled use and distributed management increases the risk of remote compromise of the device by intruders or malicious programs. Such compromises may result in the device being used to capture the user's face image and replay it to gain unauthorized access to their online accounts, possibly from a different device. Replay attacks can be highly automated and are cheap to launch worldwide, as opposed to spoofing attacks which are relatively expensive as they must be tailored to each individual victim. In this paper, we propose a technique to address replay attacks for a face recognition system by embedding a binary watermark into the captured video. Our monochrome watermark provides high contrast between the signal states, resulting in a robust signal that is practical in a wide variety of environmental conditions. It is also robust to different cameras and tolerates relative movements well. In this paper, the proposed technique is validated on different subjects using several cameras in a variety of lighting conditions. In addition, we explore the limitations of current devices and environments that can negatively impact on performance, and propose solutions to reduce the impact of these limitations.","PeriodicalId":398910,"journal":{"name":"IEEE International Conference on Identity, Security and Behavior Analysis (ISBA 2015)","volume":"4 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113957360","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2015-03-23DOI: 10.1109/ISBA.2015.7126366
Tzipora Halevi, Trishank Karthik Kuppusamy, Meghan Caiazzo, N. Memon
Biometric-based authentication is a growing trend. While this trend is enabled by the introduction of supporting technology, the use of biometrics introduces new privacy and ethical concerns about the direction of authentication. This paper explores willingness of users to share biometric information and therefore take advantage of these technological advances. Specifically, it examines, by means of an experiment, the factors that affect users' decision making when considering providing their fingerprints for a financial incentive. The study surveyed 100 participants and found that most were not willing to share their fingerprints with an e-commerce for any feasible reward. It found that while the financial incentive was a factor, perception of risk (influenced by being exposed to previous cyber-attacks) as well as the participants' self-efficacy had significant effect on the participants' decision making. The study also found that participants make context-based decision about sharing different types of personal data with different entities. The results of the study indicate that many users have concerns sharing their fingerprints with commercial companies. As new systems are being deployed, a better understanding is needed about user perceptions regarding fingerprint data sharing, so they can be better addressed by system designers in the future.
{"title":"Investigating users' readiness to trade-off biometric fingerprint data","authors":"Tzipora Halevi, Trishank Karthik Kuppusamy, Meghan Caiazzo, N. Memon","doi":"10.1109/ISBA.2015.7126366","DOIUrl":"https://doi.org/10.1109/ISBA.2015.7126366","url":null,"abstract":"Biometric-based authentication is a growing trend. While this trend is enabled by the introduction of supporting technology, the use of biometrics introduces new privacy and ethical concerns about the direction of authentication. This paper explores willingness of users to share biometric information and therefore take advantage of these technological advances. Specifically, it examines, by means of an experiment, the factors that affect users' decision making when considering providing their fingerprints for a financial incentive. The study surveyed 100 participants and found that most were not willing to share their fingerprints with an e-commerce for any feasible reward. It found that while the financial incentive was a factor, perception of risk (influenced by being exposed to previous cyber-attacks) as well as the participants' self-efficacy had significant effect on the participants' decision making. The study also found that participants make context-based decision about sharing different types of personal data with different entities. The results of the study indicate that many users have concerns sharing their fingerprints with commercial companies. As new systems are being deployed, a better understanding is needed about user perceptions regarding fingerprint data sharing, so they can be better addressed by system designers in the future.","PeriodicalId":398910,"journal":{"name":"IEEE International Conference on Identity, Security and Behavior Analysis (ISBA 2015)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125802266","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2015-03-23DOI: 10.1109/ISBA.2015.7126345
S. Idrus, E. Cherrier, C. Rosenberger, Soumik Mondal, Patrick A. H. Bours
It is accepted that the way a person types on a keyboard contains timing patterns, which can be used to classify him/her, is known as keystroke dynamics. Keystroke dynamics is a behavioural biometric modality, whose performances, however, are worse than morphological modalities such as fingerprint, iris recognition or face recognition. To cope with this, we propose to combine keystroke dynamics with soft biometrics. Soft biometrics refers to biometric characteristics that are not sufficient to authenticate a user (e.g. height, gender, skin/eye/hair colour). Concerning keystroke dynamics, three soft categories are considered: gender, age and handedness. We present different methods to combine the results of a classical keystroke dynamics system with such soft criteria. By applying simple sum and multiply rules, our experiments suggest that the combination approach performs better than the classification approach with best result of 5.41% of equal error rate. The efficiency of our approaches is illustrated on a public database.
{"title":"Keystroke dynamics performance enhancement with soft biometrics","authors":"S. Idrus, E. Cherrier, C. Rosenberger, Soumik Mondal, Patrick A. H. Bours","doi":"10.1109/ISBA.2015.7126345","DOIUrl":"https://doi.org/10.1109/ISBA.2015.7126345","url":null,"abstract":"It is accepted that the way a person types on a keyboard contains timing patterns, which can be used to classify him/her, is known as keystroke dynamics. Keystroke dynamics is a behavioural biometric modality, whose performances, however, are worse than morphological modalities such as fingerprint, iris recognition or face recognition. To cope with this, we propose to combine keystroke dynamics with soft biometrics. Soft biometrics refers to biometric characteristics that are not sufficient to authenticate a user (e.g. height, gender, skin/eye/hair colour). Concerning keystroke dynamics, three soft categories are considered: gender, age and handedness. We present different methods to combine the results of a classical keystroke dynamics system with such soft criteria. By applying simple sum and multiply rules, our experiments suggest that the combination approach performs better than the classification approach with best result of 5.41% of equal error rate. The efficiency of our approaches is illustrated on a public database.","PeriodicalId":398910,"journal":{"name":"IEEE International Conference on Identity, Security and Behavior Analysis (ISBA 2015)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116962032","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2015-03-23DOI: 10.1109/ISBA.2015.7126354
Napa Sae-Bae, N. Memon
This paper proposes a metric to measure the quality of an online signature template derived from a set of enrolled signature samples in terms of its distinctiveness against random signatures. Particularly, the proposed quality score is computed based on statistical analysis of histogram features that are used as part of an online signature representation. Experiments performed on three datasets consistently confirm the effectiveness of the proposed metric as an indication of false acceptance rate against random forgeries when the system is operated at a particular decision threshold. Finally, the use of the proposed quality metric to enforce a minimum signature strength policy in order to enhance security and reliability of the system against random forgeries is demonstrated.
{"title":"Quality of online signature templates","authors":"Napa Sae-Bae, N. Memon","doi":"10.1109/ISBA.2015.7126354","DOIUrl":"https://doi.org/10.1109/ISBA.2015.7126354","url":null,"abstract":"This paper proposes a metric to measure the quality of an online signature template derived from a set of enrolled signature samples in terms of its distinctiveness against random signatures. Particularly, the proposed quality score is computed based on statistical analysis of histogram features that are used as part of an online signature representation. Experiments performed on three datasets consistently confirm the effectiveness of the proposed metric as an indication of false acceptance rate against random forgeries when the system is operated at a particular decision threshold. Finally, the use of the proposed quality metric to enforce a minimum signature strength policy in order to enhance security and reliability of the system against random forgeries is demonstrated.","PeriodicalId":398910,"journal":{"name":"IEEE International Conference on Identity, Security and Behavior Analysis (ISBA 2015)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115708724","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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