Pub Date : 2015-05-19DOI: 10.1109/ICB.2015.7139051
Jing Li, Shuqin Long, Dan Zeng, Qijun Zhao
Reconstructing 3D face models from multiple uncalibrated 2D face images is usually done by using a single reference 3D face model or some gender/ethnicity-specific 3D face models. However, different persons, even those of the same gender or ethnicity, usually have significantly different faces in terms of their overall appearance, which forms the base of person recognition using faces. Consequently, existing 3D reference model based methods have limited capability of reconstructing 3D face models for a large variety of persons. In this paper, we propose to explore a reservoir of diverse reference models to improve the 3D face reconstruction performance. Specifically, we convert the face reconstruction problem into a multi-label segmentation problem. Its energy function is formulated from different cues, including 1) similarity between the desired output and the initial model, 2) color consistency between different views, 3) smoothness constraint on adjacent pixels, and 4) model consistency within local neighborhood. Experimental results on challenging datasets demonstrate that the proposed algorithm is capable of recovering high quality face models in both qualitative and quantitative evaluations.
{"title":"Example-based 3D face reconstruction from uncalibrated frontal and profile images","authors":"Jing Li, Shuqin Long, Dan Zeng, Qijun Zhao","doi":"10.1109/ICB.2015.7139051","DOIUrl":"https://doi.org/10.1109/ICB.2015.7139051","url":null,"abstract":"Reconstructing 3D face models from multiple uncalibrated 2D face images is usually done by using a single reference 3D face model or some gender/ethnicity-specific 3D face models. However, different persons, even those of the same gender or ethnicity, usually have significantly different faces in terms of their overall appearance, which forms the base of person recognition using faces. Consequently, existing 3D reference model based methods have limited capability of reconstructing 3D face models for a large variety of persons. In this paper, we propose to explore a reservoir of diverse reference models to improve the 3D face reconstruction performance. Specifically, we convert the face reconstruction problem into a multi-label segmentation problem. Its energy function is formulated from different cues, including 1) similarity between the desired output and the initial model, 2) color consistency between different views, 3) smoothness constraint on adjacent pixels, and 4) model consistency within local neighborhood. Experimental results on challenging datasets demonstrate that the proposed algorithm is capable of recovering high quality face models in both qualitative and quantitative evaluations.","PeriodicalId":237372,"journal":{"name":"2015 International Conference on Biometrics (ICB)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128562594","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 : 2015-05-19DOI: 10.1109/ICB.2015.7139040
Thomas Bergmüller, Eleftherios Christopoulos, Martin Schnoell, A. Uhl
Rating a compression algorithm's performance is usually done in experimental studies, where researchers have frequently used JPEG pre-compressed data. It is not clear yet, whether results of such compression experiments are reliable if conducted from pre-compressed data. To investigate this issue, we study the impact of using pre-compressed data on iris segmentation and evaluate the relation between iris segmentation performance and general quality metrics. Furthermore we propose a method to overcome potential problems in case using pre-compressed data sets cannot be avoided, e.g. for reasons of ground-truth availability.
{"title":"Recompression effects in iris segmentation","authors":"Thomas Bergmüller, Eleftherios Christopoulos, Martin Schnoell, A. Uhl","doi":"10.1109/ICB.2015.7139040","DOIUrl":"https://doi.org/10.1109/ICB.2015.7139040","url":null,"abstract":"Rating a compression algorithm's performance is usually done in experimental studies, where researchers have frequently used JPEG pre-compressed data. It is not clear yet, whether results of such compression experiments are reliable if conducted from pre-compressed data. To investigate this issue, we study the impact of using pre-compressed data on iris segmentation and evaluate the relation between iris segmentation performance and general quality metrics. Furthermore we propose a method to overcome potential problems in case using pre-compressed data sets cannot be avoided, e.g. for reasons of ground-truth availability.","PeriodicalId":237372,"journal":{"name":"2015 International Conference on Biometrics (ICB)","volume":"74 5-6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131791660","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 : 2015-05-19DOI: 10.1109/ICB.2015.7139112
Dayong Wang, Anil K. Jain
Face retrieval is an enabling technology for many applications, including automatic face annotation, deduplication, and surveillance. In this paper, we propose a face retrieval system which combines a k-NN search procedure with a COTS matcher (PittPatt1) in a cascaded manner. In particular, given a query face, we first pre-filter the gallery set and find the top-k most similar faces for the query image by using deep facial features that are learned with a deep convolutional neural network. The top-k most similar faces are then re-ranked based on score-level fusion of the similarities between deep features and the COTS matcher. To further boost the retrieval performance, we develop a manifold ranking algorithm. The proposed face retrieval system is evaluated on two large-scale face image databases: (i) a web face image database, which consists of over 3, 880 query images of 1, 507 subjects and a gallery of 5, 000, 000 faces, and (ii) a mugshot database, which consists of 1, 000 query images of 1, 000 subjects and a gallery of 1, 000, 000 faces. Experimental results demonstrate that the proposed face retrieval system can simultaneously improve the retrieval performance (CMC and precision-recall) and scalability for large-scale face retrieval problems.
{"title":"Face retriever: Pre-filtering the gallery via deep neural net","authors":"Dayong Wang, Anil K. Jain","doi":"10.1109/ICB.2015.7139112","DOIUrl":"https://doi.org/10.1109/ICB.2015.7139112","url":null,"abstract":"Face retrieval is an enabling technology for many applications, including automatic face annotation, deduplication, and surveillance. In this paper, we propose a face retrieval system which combines a k-NN search procedure with a COTS matcher (PittPatt1) in a cascaded manner. In particular, given a query face, we first pre-filter the gallery set and find the top-k most similar faces for the query image by using deep facial features that are learned with a deep convolutional neural network. The top-k most similar faces are then re-ranked based on score-level fusion of the similarities between deep features and the COTS matcher. To further boost the retrieval performance, we develop a manifold ranking algorithm. The proposed face retrieval system is evaluated on two large-scale face image databases: (i) a web face image database, which consists of over 3, 880 query images of 1, 507 subjects and a gallery of 5, 000, 000 faces, and (ii) a mugshot database, which consists of 1, 000 query images of 1, 000 subjects and a gallery of 1, 000, 000 faces. Experimental results demonstrate that the proposed face retrieval system can simultaneously improve the retrieval performance (CMC and precision-recall) and scalability for large-scale face retrieval problems.","PeriodicalId":237372,"journal":{"name":"2015 International Conference on Biometrics (ICB)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128144388","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 : 2015-05-19DOI: 10.1109/ICB.2015.7139047
Rubén Tolosana, R. Vera-Rodríguez, J. Ortega-Garcia, Julian Fierrez
Due to the technological evolution and the increasing popularity of smartphones, people can access an application with many different devices. This device interoperability is a very challenging problem for biometrics. In this paper we focus on inter-operability device compensation for on-line signature verification. The proposed approach is based on two main stages. The first one is a preprocessing stage where data acquired from different devices are processed in order to normalize the signals in similar ranges. The second one is based on a feature selection of time functions taking into account the inter-operability device comparisons in order to select features which are robust in these conditions. The experimental work has been carried out with Biosecure database using a Wacom tablet (DS2) and a PDA tablet (DS3), and the system developed is based on dynamic time warping (DTW) elastic measure over the selected time functions. The performance of the proposed system is very similar compared to an ideal system. Also, the proposed approach provides average relative improvements for the cases of inter-operability comparisons of 26.5% for random forgeries and, around 14.2% for the case of skilled forgeries comparing the results with the case of having a system specifically tuned for each device, proving the robustness of the proposed approach. These results open the door for future works using devices as smartphones or tablets, commonly used nowadays.
{"title":"Optimal feature selection and inter-operability compensation for on-line biometric signature authentication","authors":"Rubén Tolosana, R. Vera-Rodríguez, J. Ortega-Garcia, Julian Fierrez","doi":"10.1109/ICB.2015.7139047","DOIUrl":"https://doi.org/10.1109/ICB.2015.7139047","url":null,"abstract":"Due to the technological evolution and the increasing popularity of smartphones, people can access an application with many different devices. This device interoperability is a very challenging problem for biometrics. In this paper we focus on inter-operability device compensation for on-line signature verification. The proposed approach is based on two main stages. The first one is a preprocessing stage where data acquired from different devices are processed in order to normalize the signals in similar ranges. The second one is based on a feature selection of time functions taking into account the inter-operability device comparisons in order to select features which are robust in these conditions. The experimental work has been carried out with Biosecure database using a Wacom tablet (DS2) and a PDA tablet (DS3), and the system developed is based on dynamic time warping (DTW) elastic measure over the selected time functions. The performance of the proposed system is very similar compared to an ideal system. Also, the proposed approach provides average relative improvements for the cases of inter-operability comparisons of 26.5% for random forgeries and, around 14.2% for the case of skilled forgeries comparing the results with the case of having a system specifically tuned for each device, proving the robustness of the proposed approach. These results open the door for future works using devices as smartphones or tablets, commonly used nowadays.","PeriodicalId":237372,"journal":{"name":"2015 International Conference on Biometrics (ICB)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130140864","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}
Research in face recognition under constrained environment has achieved an acceptable level of performance. However, there is a significant scope for improving face recognition capabilities in unconstrained environment including surveillance videos. Such videos are likely to record multiple people within the field of view. Face recognition in such a setting poses a set of challenges including unreliable face detection, multiple subjects performing different actions, low resolution, and sensor interoperability. In general, existing video face databases contain one subject in a video sequence. However, real world video sequences are more challenging and generally contain more than one person in a video. Therefore, in this paper, we provide an annotated crowd video face (ACVF-2014) database, along with face landmark information to encourage research in this important problem. The ACVF-2014 dataset contains 201 videos of 133 subjects where each video contains multiple subjects. We provide two distinct use-case scenarios, define their experimental protocols, and report baseline verification results using OpenBR and FaceVACS. The results show that both the baseline results do not yield more than 0.16 genuine accept rate @ 0.01 false accept rate. A software package is also developed to help researchers evaluate their systems using the defined protocols.
{"title":"Annotated crowd video face database","authors":"Tejas I. Dhamecha, Priyanka Verma, Mahek Shah, Richa Singh, Mayank Vatsa","doi":"10.1109/ICB.2015.7139083","DOIUrl":"https://doi.org/10.1109/ICB.2015.7139083","url":null,"abstract":"Research in face recognition under constrained environment has achieved an acceptable level of performance. However, there is a significant scope for improving face recognition capabilities in unconstrained environment including surveillance videos. Such videos are likely to record multiple people within the field of view. Face recognition in such a setting poses a set of challenges including unreliable face detection, multiple subjects performing different actions, low resolution, and sensor interoperability. In general, existing video face databases contain one subject in a video sequence. However, real world video sequences are more challenging and generally contain more than one person in a video. Therefore, in this paper, we provide an annotated crowd video face (ACVF-2014) database, along with face landmark information to encourage research in this important problem. The ACVF-2014 dataset contains 201 videos of 133 subjects where each video contains multiple subjects. We provide two distinct use-case scenarios, define their experimental protocols, and report baseline verification results using OpenBR and FaceVACS. The results show that both the baseline results do not yield more than 0.16 genuine accept rate @ 0.01 false accept rate. A software package is also developed to help researchers evaluate their systems using the defined protocols.","PeriodicalId":237372,"journal":{"name":"2015 International Conference on Biometrics (ICB)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133818090","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 : 2015-05-19DOI: 10.1109/ICB.2015.7139080
Ran Xian, Liao Ni, Wenxin Li
Finger vein recognition is a newly developed and promising biometrics technology. To facilitate evaluation in this area and study state-of-the-art performance of the finger vein recognition algorithms, we organized The ICB-2015 Competition on Finger Vein Recognition (ICFVR2015). This competition is held on a general recognition algorithm evaluation platform called RATE, with 3 data sets collected from volunteers and actual usage. 7 algorithms were finally submitted, with the best EER achieving 0.375%. This paper will first introduce the organization of the competition and RATE, then describe data sets and test protocols, and finally present results of the competition.
{"title":"The ICB-2015 Competition on Finger Vein Recognition","authors":"Ran Xian, Liao Ni, Wenxin Li","doi":"10.1109/ICB.2015.7139080","DOIUrl":"https://doi.org/10.1109/ICB.2015.7139080","url":null,"abstract":"Finger vein recognition is a newly developed and promising biometrics technology. To facilitate evaluation in this area and study state-of-the-art performance of the finger vein recognition algorithms, we organized The ICB-2015 Competition on Finger Vein Recognition (ICFVR2015). This competition is held on a general recognition algorithm evaluation platform called RATE, with 3 data sets collected from volunteers and actual usage. 7 algorithms were finally submitted, with the best EER achieving 0.375%. This paper will first introduce the organization of the competition and RATE, then describe data sets and test protocols, and finally present results of the competition.","PeriodicalId":237372,"journal":{"name":"2015 International Conference on Biometrics (ICB)","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122754899","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 : 2015-05-19DOI: 10.1109/ICB.2015.7139063
Phumpat Ruangsakul, V. Areekul, Krisada Phromsuthirak, Arucha Rungchokanun
In this work, we present a latent fingerprint segmentation algorithm based on spatial-frequency domain analysis. The algorithm arranges the overlapped block-based Fourier coefficients into groups of frequency and orientation subbands, called Rearranged Fourier Subband (RFS). The RFS reveals latent fingerprint spectra in only a limited number of subbands. The algorithm then boosts, sorts, and extracts, from complex background and noise, the latent fingerprint spectra in the RFS subbands. Several experiments are evaluated based on ground truth comparison, feature extraction, and latent matching on the NIST SD27 latent database. Our experimental results show that the proposed algorithm achieves better accuracy compared to those of the published automatic segmentation algorithms.
{"title":"Latent fingerprints segmentation based on Rearranged Fourier Subbands","authors":"Phumpat Ruangsakul, V. Areekul, Krisada Phromsuthirak, Arucha Rungchokanun","doi":"10.1109/ICB.2015.7139063","DOIUrl":"https://doi.org/10.1109/ICB.2015.7139063","url":null,"abstract":"In this work, we present a latent fingerprint segmentation algorithm based on spatial-frequency domain analysis. The algorithm arranges the overlapped block-based Fourier coefficients into groups of frequency and orientation subbands, called Rearranged Fourier Subband (RFS). The RFS reveals latent fingerprint spectra in only a limited number of subbands. The algorithm then boosts, sorts, and extracts, from complex background and noise, the latent fingerprint spectra in the RFS subbands. Several experiments are evaluated based on ground truth comparison, feature extraction, and latent matching on the NIST SD27 latent database. Our experimental results show that the proposed algorithm achieves better accuracy compared to those of the published automatic segmentation algorithms.","PeriodicalId":237372,"journal":{"name":"2015 International Conference on Biometrics (ICB)","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116932740","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 : 2015-05-19DOI: 10.1109/ICB.2015.7139044
K. Raja, Ramachandra Raghavendra, Martin Stokkenes, C. Busch
Secure authentication for smartphones is becoming important for many applications such as financial transactions. Until today PIN and password authentication are the most commonly used methods for smartphone access control. Specifically for a PIN and limited length passwords, the level of security is low and thus can be compromised easily. In this work, we propose a multi-modal biometric system, which uses face, periocular and iris biometric characteristics for authentication. The proposed system is tested on two different devices - Samsung Galaxy S5 smartphone and Samsung Galaxy Note 10.1 tablet. An extensive set of experiments conducted using the proposed system shows the applicability for secure authentication scenarios. The proposed system is tested using uni-modal and multi-modal approach. An Equal Error Rate (EER) of 0.68% is obtained from the experiments validating the robust performance of the proposed system.
{"title":"Multi-modal authentication system for smartphones using face, iris and periocular","authors":"K. Raja, Ramachandra Raghavendra, Martin Stokkenes, C. Busch","doi":"10.1109/ICB.2015.7139044","DOIUrl":"https://doi.org/10.1109/ICB.2015.7139044","url":null,"abstract":"Secure authentication for smartphones is becoming important for many applications such as financial transactions. Until today PIN and password authentication are the most commonly used methods for smartphone access control. Specifically for a PIN and limited length passwords, the level of security is low and thus can be compromised easily. In this work, we propose a multi-modal biometric system, which uses face, periocular and iris biometric characteristics for authentication. The proposed system is tested on two different devices - Samsung Galaxy S5 smartphone and Samsung Galaxy Note 10.1 tablet. An extensive set of experiments conducted using the proposed system shows the applicability for secure authentication scenarios. The proposed system is tested using uni-modal and multi-modal approach. An Equal Error Rate (EER) of 0.68% is obtained from the experiments validating the robust performance of the proposed system.","PeriodicalId":237372,"journal":{"name":"2015 International Conference on Biometrics (ICB)","volume":"80 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114993553","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 : 2015-05-19DOI: 10.1109/ICB.2015.7139056
Pedro Tome, S. Marcel
The vulnerability of palm vein recognition to spoofing attacks is studied in this paper. A collection of spoofing palm vein images has been created from real palm vein samples. Palm vein images are printed using a commercial printer and then, presented at a contactless palm vein sensor. Experiments are carried out using an extensible framework, which allows fair and reproducible benchmarks. Results are presented comparing two automatic segmentations. Experimental results lead to a spoofing false accept rate of 65%, thus showing that palm vein biometrics is vulnerable to spoofing attacks, pointing out the importance to investigate countermeasures against this type of fraudulent actions. A study based on the number of the enrolment samples is also reported, demonstrating a relationship between the number of enrolment samples and the vulnerability of the system to spoofing.
{"title":"On the vulnerability of palm vein recognition to spoofing attacks","authors":"Pedro Tome, S. Marcel","doi":"10.1109/ICB.2015.7139056","DOIUrl":"https://doi.org/10.1109/ICB.2015.7139056","url":null,"abstract":"The vulnerability of palm vein recognition to spoofing attacks is studied in this paper. A collection of spoofing palm vein images has been created from real palm vein samples. Palm vein images are printed using a commercial printer and then, presented at a contactless palm vein sensor. Experiments are carried out using an extensible framework, which allows fair and reproducible benchmarks. Results are presented comparing two automatic segmentations. Experimental results lead to a spoofing false accept rate of 65%, thus showing that palm vein biometrics is vulnerable to spoofing attacks, pointing out the importance to investigate countermeasures against this type of fraudulent actions. A study based on the number of the enrolment samples is also reported, demonstrating a relationship between the number of enrolment samples and the vulnerability of the system to spoofing.","PeriodicalId":237372,"journal":{"name":"2015 International Conference on Biometrics (ICB)","volume":"97 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115664941","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 : 2015-05-19DOI: 10.1109/ICB.2015.7139067
Pedro Tome, Ramachandra Raghavendra, C. Busch, Santosh Tirunagari, N. Poh, B. H. Shekar, Diego Gragnaniello, Carlo Sansone, L. Verdoliva, S. Marcel
The vulnerability of finger vein recognition to spoofing attacks has emerged as a crucial security problem in the recent years mainly due to the high security applications where biometric technology is used. Recent works shown that finger vein biometrics is vulnerable to spoofing attacks, pointing out the importance to investigate counter-measures against this type of fraudulent actions. The goal of the 1st Competition on Counter Measures to Finger Vein Spoofing Attacks is to challenge researchers to create counter-measures that can detect printed attacks effectively. The submitted approaches are evaluated on the Spoofing-Attack Finger Vein Database and the achieved results are presented in this paper.
{"title":"The 1st Competition on Counter Measures to Finger Vein Spoofing Attacks","authors":"Pedro Tome, Ramachandra Raghavendra, C. Busch, Santosh Tirunagari, N. Poh, B. H. Shekar, Diego Gragnaniello, Carlo Sansone, L. Verdoliva, S. Marcel","doi":"10.1109/ICB.2015.7139067","DOIUrl":"https://doi.org/10.1109/ICB.2015.7139067","url":null,"abstract":"The vulnerability of finger vein recognition to spoofing attacks has emerged as a crucial security problem in the recent years mainly due to the high security applications where biometric technology is used. Recent works shown that finger vein biometrics is vulnerable to spoofing attacks, pointing out the importance to investigate counter-measures against this type of fraudulent actions. The goal of the 1st Competition on Counter Measures to Finger Vein Spoofing Attacks is to challenge researchers to create counter-measures that can detect printed attacks effectively. The submitted approaches are evaluated on the Spoofing-Attack Finger Vein Database and the achieved results are presented in this paper.","PeriodicalId":237372,"journal":{"name":"2015 International Conference on Biometrics (ICB)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123980556","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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