Pub Date : 2014-12-09DOI: 10.1109/CIBIM.2014.7015437
Z. Akhtar, Amr Ahmed, Ç. Erdem, G. Foresti
Being biological tissues in nature, all biometric traits undergo aging. Aging has profound effects on facial biometrics as it causes change in shape and texture. However aging remain an under-studied problem in comparison to facial variations due to pose, illumination and expression changes. A commonly adopted solution in the state-of-the-art is the virtual template synthesis for aging and de-aging transformations involving complex 3D modelling techniques. These methods are also prone to estimation errors in the synthesis. Another viable solution is to continuously adapt the template to the temporal variation (aging) of the query data. Though efficacy of template update procedures has been proven for expression, lightning and pose variations, the use of template update for facial aging has not received much attention so far. This paper investigates the use of template update procedures for temporal variance due to the facial aging process. Experimental evaluations on FGNET and MORPH aging database using commercial VeriLook face recognition engine demonstrate that continuous template updating is an effective and simple way to adapt to variations due to the aging process.
{"title":"Biometric template update under facial aging","authors":"Z. Akhtar, Amr Ahmed, Ç. Erdem, G. Foresti","doi":"10.1109/CIBIM.2014.7015437","DOIUrl":"https://doi.org/10.1109/CIBIM.2014.7015437","url":null,"abstract":"Being biological tissues in nature, all biometric traits undergo aging. Aging has profound effects on facial biometrics as it causes change in shape and texture. However aging remain an under-studied problem in comparison to facial variations due to pose, illumination and expression changes. A commonly adopted solution in the state-of-the-art is the virtual template synthesis for aging and de-aging transformations involving complex 3D modelling techniques. These methods are also prone to estimation errors in the synthesis. Another viable solution is to continuously adapt the template to the temporal variation (aging) of the query data. Though efficacy of template update procedures has been proven for expression, lightning and pose variations, the use of template update for facial aging has not received much attention so far. This paper investigates the use of template update procedures for temporal variance due to the facial aging process. Experimental evaluations on FGNET and MORPH aging database using commercial VeriLook face recognition engine demonstrate that continuous template updating is an effective and simple way to adapt to variations due to the aging process.","PeriodicalId":432938,"journal":{"name":"2014 IEEE Symposium on Computational Intelligence in Biometrics and Identity Management (CIBIM)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133554907","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 : 2014-12-01DOI: 10.1109/CIBIM.2014.7015460
N. Poh, Rita Wong, G. Marcialis
In order to render a biometric system robust against malicious tampering, it is important to understand the different types of attack and their impact as observed by the liveness and matching scores. In this study, we consider zero-effort impostor attack (referred to as the Z-attack), nonzero-effort impostor attack such as presentation attack or spoofing (S-attack), and other categories of attack involving tampering at the template level (U- and T-attacks). In order to elucidate the impact of all possible attacks, we (1) introduce the concepts of source of origin and symmetric biometric matchers, and (2) subsequently group the attacks into four categories. These views not only improve the understanding of the nature of different attacks but also turn out to ease the design of the classification problem. Following this analysis, we design a novel classification scheme that can take full advantage of the attack-specific data characteristics. Two realisations of the scheme, namely, a mixture of linear classifiers, and a Gaussian Copula-based Bayesian classifier, turn out to outperform a strong baseline classifier based on SVM, as supported by fingerprint spoofing experiments.
{"title":"Toward an attack-sensitive tamper-resistant biometric recognition with a symmetric matcher: A fingerprint case study","authors":"N. Poh, Rita Wong, G. Marcialis","doi":"10.1109/CIBIM.2014.7015460","DOIUrl":"https://doi.org/10.1109/CIBIM.2014.7015460","url":null,"abstract":"In order to render a biometric system robust against malicious tampering, it is important to understand the different types of attack and their impact as observed by the liveness and matching scores. In this study, we consider zero-effort impostor attack (referred to as the Z-attack), nonzero-effort impostor attack such as presentation attack or spoofing (S-attack), and other categories of attack involving tampering at the template level (U- and T-attacks). In order to elucidate the impact of all possible attacks, we (1) introduce the concepts of source of origin and symmetric biometric matchers, and (2) subsequently group the attacks into four categories. These views not only improve the understanding of the nature of different attacks but also turn out to ease the design of the classification problem. Following this analysis, we design a novel classification scheme that can take full advantage of the attack-specific data characteristics. Two realisations of the scheme, namely, a mixture of linear classifiers, and a Gaussian Copula-based Bayesian classifier, turn out to outperform a strong baseline classifier based on SVM, as supported by fingerprint spoofing experiments.","PeriodicalId":432938,"journal":{"name":"2014 IEEE Symposium on Computational Intelligence in Biometrics and Identity Management (CIBIM)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125074349","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 : 2014-12-01DOI: 10.1109/CIBIM.2014.7015451
D. Gorodnichy, Eric Granger
This paper concerns the problem of real-time watch-list screening (WLS) using face recognition (FR) technology. The risk of flagging innocent travellers can be very high when deploying a FR system for WLS since: (i) faces captured in surveillance video vary considerably due to pose, expression, illumination, and camera inter-operability; (ii) reference images of targets in a watch-list are typically of limited quality or quantity; (iii) the performance of FR systems may vary significantly from one individual to another (according to socalled “biometric menagerie” phenomenon); (iv) the number of travellers drastically exceeds the number of target people in a watch-list; and finally and most critically, (v) due to the nature of optics, images of faces captured by video-surveillance cameras are focused and sharp only over a very short period of time if ever at all. Existing evaluation frameworks were originally developed for spatial face identification from still images, and do not allow one to properly examine the suitability of the FR technology for WLS with respect to the above listed risk factors intrinsically present in any video surveillance application. This paper introduces the target-based multi-level FR performance evaluation framework that is suitable for WLS. According to the framework, Level 0 (face detection analysis) deals with the system's ability to process low resolution faces. Level 1 (transaction-based analysis) deals with the ability to match faces in open-set problems, where target vs. non-target distributions are unbalanced. Level 2 (subject-based analysis) deals with robustness of the system to different types of target individuals. Finally, Level 3 (spatio-temporal analysis) allows one to examine the overall FR system discrimination by means of accumulating the recognition decision confidence over a face track, which can be used for developing more robust intelligent decision-making schemes including face triaging.The results from testing a commercial state-of-art COTS FR product on a public video data-set are shown to illustrate the benefits of this framework.
{"title":"Target-based evaluation of face recognition technology for video surveillance applications","authors":"D. Gorodnichy, Eric Granger","doi":"10.1109/CIBIM.2014.7015451","DOIUrl":"https://doi.org/10.1109/CIBIM.2014.7015451","url":null,"abstract":"This paper concerns the problem of real-time watch-list screening (WLS) using face recognition (FR) technology. The risk of flagging innocent travellers can be very high when deploying a FR system for WLS since: (i) faces captured in surveillance video vary considerably due to pose, expression, illumination, and camera inter-operability; (ii) reference images of targets in a watch-list are typically of limited quality or quantity; (iii) the performance of FR systems may vary significantly from one individual to another (according to socalled “biometric menagerie” phenomenon); (iv) the number of travellers drastically exceeds the number of target people in a watch-list; and finally and most critically, (v) due to the nature of optics, images of faces captured by video-surveillance cameras are focused and sharp only over a very short period of time if ever at all. Existing evaluation frameworks were originally developed for spatial face identification from still images, and do not allow one to properly examine the suitability of the FR technology for WLS with respect to the above listed risk factors intrinsically present in any video surveillance application. This paper introduces the target-based multi-level FR performance evaluation framework that is suitable for WLS. According to the framework, Level 0 (face detection analysis) deals with the system's ability to process low resolution faces. Level 1 (transaction-based analysis) deals with the ability to match faces in open-set problems, where target vs. non-target distributions are unbalanced. Level 2 (subject-based analysis) deals with robustness of the system to different types of target individuals. Finally, Level 3 (spatio-temporal analysis) allows one to examine the overall FR system discrimination by means of accumulating the recognition decision confidence over a face track, which can be used for developing more robust intelligent decision-making schemes including face triaging.The results from testing a commercial state-of-art COTS FR product on a public video data-set are shown to illustrate the benefits of this framework.","PeriodicalId":432938,"journal":{"name":"2014 IEEE Symposium on Computational Intelligence in Biometrics and Identity Management (CIBIM)","volume":"2020 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123423224","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 : 2014-12-01DOI: 10.1109/CIBIM.2014.7015462
Yanzhu Liu, X. Li, A. Kong
Identifying individuals in evidence images (e.g. child sexual abuse and masked gunmen), where their faces are covered or obstructed, is a challenging task. Skin mark patterns and blood vessel patterns have been proposed as biometrics to overcome this challenge, but their clarity depends on the quality of evidence images. However, evidence images are very likely compressed by the JPEG method, which is widely installed in digital cameras. To remove blocking artifacts in skin images and restore the original clarity for forensic analysis, a knowledge-based deblocking method, which replaces compressed blocks in evidence images with uncompressed blocks from a large skin image database, was proposed. Experimental results demonstrated that this method is effective and performs better than other deblocking methods that were designed for generic images. The search for optimal uncompressed blocks in a large skin image database is computationally demanding. Ideally, this computational burden should be reduced since even in one single case, the number of evidence images can be numerous. This paper first studies statistical characteristics of skin images. Making use of this information, hash functions, bitwise ℓ1-minimization, and a parallel scheme were developed to speed up the knowledge-based deblocking method. Experimental results demonstrate that the proposed computational techniques speed up the knowledge-based deblocking method more than 150% on average.
{"title":"Speeding up the knowledge-based deblocking method for efficient forensic analysis","authors":"Yanzhu Liu, X. Li, A. Kong","doi":"10.1109/CIBIM.2014.7015462","DOIUrl":"https://doi.org/10.1109/CIBIM.2014.7015462","url":null,"abstract":"Identifying individuals in evidence images (e.g. child sexual abuse and masked gunmen), where their faces are covered or obstructed, is a challenging task. Skin mark patterns and blood vessel patterns have been proposed as biometrics to overcome this challenge, but their clarity depends on the quality of evidence images. However, evidence images are very likely compressed by the JPEG method, which is widely installed in digital cameras. To remove blocking artifacts in skin images and restore the original clarity for forensic analysis, a knowledge-based deblocking method, which replaces compressed blocks in evidence images with uncompressed blocks from a large skin image database, was proposed. Experimental results demonstrated that this method is effective and performs better than other deblocking methods that were designed for generic images. The search for optimal uncompressed blocks in a large skin image database is computationally demanding. Ideally, this computational burden should be reduced since even in one single case, the number of evidence images can be numerous. This paper first studies statistical characteristics of skin images. Making use of this information, hash functions, bitwise ℓ1-minimization, and a parallel scheme were developed to speed up the knowledge-based deblocking method. Experimental results demonstrate that the proposed computational techniques speed up the knowledge-based deblocking method more than 150% on average.","PeriodicalId":432938,"journal":{"name":"2014 IEEE Symposium on Computational Intelligence in Biometrics and Identity Management (CIBIM)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126675099","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 : 2014-12-01DOI: 10.1109/CIBIM.2014.7015464
Antoine Deblonde
In this paper, we propose a novel method for fingerprint indexing based on local patterns of ridge flow centered on minutiae. These local descriptors are projected on a learned dictionary of ridge flow patches, with a sparsity-inducing algorithm. We show that this sparse decomposition allows to replace the ridge flow patches by a compressed signature with a reduced loss of accuracy. We experimented the combination of these descriptors with the formerly known Minutiae Cylinder Code (MCC) descriptor, that provides another kind of local information. Then, we show that the combination of these descriptors performs well for fast nearest neighbor search algorithms based on Locality-Sensitive Hashing (LSH), and allows to either to improve the accuracy of the state-of-the-art algorithm, or to improve its computational efficiency.
{"title":"Fingerprint indexing through sparse decomposition of ridge flow patches","authors":"Antoine Deblonde","doi":"10.1109/CIBIM.2014.7015464","DOIUrl":"https://doi.org/10.1109/CIBIM.2014.7015464","url":null,"abstract":"In this paper, we propose a novel method for fingerprint indexing based on local patterns of ridge flow centered on minutiae. These local descriptors are projected on a learned dictionary of ridge flow patches, with a sparsity-inducing algorithm. We show that this sparse decomposition allows to replace the ridge flow patches by a compressed signature with a reduced loss of accuracy. We experimented the combination of these descriptors with the formerly known Minutiae Cylinder Code (MCC) descriptor, that provides another kind of local information. Then, we show that the combination of these descriptors performs well for fast nearest neighbor search algorithms based on Locality-Sensitive Hashing (LSH), and allows to either to improve the accuracy of the state-of-the-art algorithm, or to improve its computational efficiency.","PeriodicalId":432938,"journal":{"name":"2014 IEEE Symposium on Computational Intelligence in Biometrics and Identity Management (CIBIM)","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130655282","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 : 2014-12-01DOI: 10.1109/CIBIM.2014.7015440
R. D. Labati, V. Piuri, R. Sassi, F. Scotti, Gianluca Sforza
The diffusion of wearable and mobile devices for the acquisition and analysis of cardiac signals drastically increased the possible applicative scenarios of biometric systems based on electrocardiography (ECG). Moreover, such devices allow for comfortable and unconstrained acquisitions of ECG signals for relevant time spans of tens of hours, thus making these physiological signals particularly attractive biometric traits for continuous authentication applications. In this context, recent studies showed that the QRS complex is the most stable component of the ECG signal, but the accuracy of the authentication degrades over time, due to significant variations in the patterns for each individual. Adaptive techniques for automatic template update can therefore become enabling technologies for continuous authentication systems based on ECG characteristics.
{"title":"Adaptive ECG biometric recognition: a study on re-enrollment methods for QRS signals","authors":"R. D. Labati, V. Piuri, R. Sassi, F. Scotti, Gianluca Sforza","doi":"10.1109/CIBIM.2014.7015440","DOIUrl":"https://doi.org/10.1109/CIBIM.2014.7015440","url":null,"abstract":"The diffusion of wearable and mobile devices for the acquisition and analysis of cardiac signals drastically increased the possible applicative scenarios of biometric systems based on electrocardiography (ECG). Moreover, such devices allow for comfortable and unconstrained acquisitions of ECG signals for relevant time spans of tens of hours, thus making these physiological signals particularly attractive biometric traits for continuous authentication applications. In this context, recent studies showed that the QRS complex is the most stable component of the ECG signal, but the accuracy of the authentication degrades over time, due to significant variations in the patterns for each individual. Adaptive techniques for automatic template update can therefore become enabling technologies for continuous authentication systems based on ECG characteristics.","PeriodicalId":432938,"journal":{"name":"2014 IEEE Symposium on Computational Intelligence in Biometrics and Identity Management (CIBIM)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131759852","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 : 2014-12-01DOI: 10.1109/CIBIM.2014.7015463
Bureera Sabir, Usman Qamar, Abdul Wahab Muzzafar
This research is aimed to develop an ontology based on UMLS to the domain of urinal tract infection that contains information regarding definitions, synonyms, relations and semantic types from various biomedical vocabularies and to display them in a live portal (http://115.167.72.12/knowledgebase/index.php ) the resulting ontology was then formally evaluated and domain expert reviews are applied to measure ontology correctness in terms of structure and content.
{"title":"Ontology development and evaluation for urinal tract infection","authors":"Bureera Sabir, Usman Qamar, Abdul Wahab Muzzafar","doi":"10.1109/CIBIM.2014.7015463","DOIUrl":"https://doi.org/10.1109/CIBIM.2014.7015463","url":null,"abstract":"This research is aimed to develop an ontology based on UMLS to the domain of urinal tract infection that contains information regarding definitions, synonyms, relations and semantic types from various biomedical vocabularies and to display them in a live portal (http://115.167.72.12/knowledgebase/index.php ) the resulting ontology was then formally evaluated and domain expert reviews are applied to measure ontology correctness in terms of structure and content.","PeriodicalId":432938,"journal":{"name":"2014 IEEE Symposium on Computational Intelligence in Biometrics and Identity Management (CIBIM)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124095361","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 : 2014-12-01DOI: 10.1109/CIBIM.2014.7015456
Eyüp Burak Ceyhan, Ş. Sağiroğlu
In the literature, there are some studies which investigate if there is a relationship between fingerprint and gender or not. In these studies, this relationship is examined based on some vectorial parts of fingerprints. The main problem in these studies is the lack of data, depending on ethnical background and country, and there is not an exact finding of true classification results. It is known that fingerprints show difference in males and females, and it is explained that women's line details are thin whereas men's line details are thick. However, the statistical studies, which have been made to prove the relationship between fingerprint and gender, have not investigated if the hypothesis is true for all ethnical backgrounds. In this study, we have examined if gender inference can be made only through fingerprint feature vectors, which belong to Turkish subjects, by using our database consisting of Naive Bayes, kNN, Decision Tree and Support Vector Machine learning algorithms. By using Naive Bayes algorithm, the success of the gender classification is found as 95.3%. This ratio has not been obtained before for “gender inference from fingerprint” in the literature. Therefore, this study can be useful for criminal cases.
{"title":"Gender inference within Turkish population by using only fingerprint feature vectors","authors":"Eyüp Burak Ceyhan, Ş. Sağiroğlu","doi":"10.1109/CIBIM.2014.7015456","DOIUrl":"https://doi.org/10.1109/CIBIM.2014.7015456","url":null,"abstract":"In the literature, there are some studies which investigate if there is a relationship between fingerprint and gender or not. In these studies, this relationship is examined based on some vectorial parts of fingerprints. The main problem in these studies is the lack of data, depending on ethnical background and country, and there is not an exact finding of true classification results. It is known that fingerprints show difference in males and females, and it is explained that women's line details are thin whereas men's line details are thick. However, the statistical studies, which have been made to prove the relationship between fingerprint and gender, have not investigated if the hypothesis is true for all ethnical backgrounds. In this study, we have examined if gender inference can be made only through fingerprint feature vectors, which belong to Turkish subjects, by using our database consisting of Naive Bayes, kNN, Decision Tree and Support Vector Machine learning algorithms. By using Naive Bayes algorithm, the success of the gender classification is found as 95.3%. This ratio has not been obtained before for “gender inference from fingerprint” in the literature. Therefore, this study can be useful for criminal cases.","PeriodicalId":432938,"journal":{"name":"2014 IEEE Symposium on Computational Intelligence in Biometrics and Identity Management (CIBIM)","volume":"123 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121428254","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 : 2014-12-01DOI: 10.1109/CIBIM.2014.7015448
Jing Li, Bin Li, Yong Xu, Kaixuan Lu, Ke Yan, Lunke Fei
In this paper, we propose an effective method for disguised face detection and recognition under the complex background. This method consists of two stages. The first stage determines whether the object is a person. In this stage, we propose the first-dynamic-then-static foreground object detection strategy. This strategy exploits the updated learning-based codebook model for moving object detection and uses the Local Binary Patterns (LBP) + Histogram of Oriented Gradients (HOG) feature-based head-shoulder detection for static target detection. The second stage determines whether the face is disguised and the classes of disguises. Experiments show that our method can detect disguised faces in real time under the complex background and achieve acceptable disguised face recognition rate.
{"title":"Disguised face detection and recognition under the complex background","authors":"Jing Li, Bin Li, Yong Xu, Kaixuan Lu, Ke Yan, Lunke Fei","doi":"10.1109/CIBIM.2014.7015448","DOIUrl":"https://doi.org/10.1109/CIBIM.2014.7015448","url":null,"abstract":"In this paper, we propose an effective method for disguised face detection and recognition under the complex background. This method consists of two stages. The first stage determines whether the object is a person. In this stage, we propose the first-dynamic-then-static foreground object detection strategy. This strategy exploits the updated learning-based codebook model for moving object detection and uses the Local Binary Patterns (LBP) + Histogram of Oriented Gradients (HOG) feature-based head-shoulder detection for static target detection. The second stage determines whether the face is disguised and the classes of disguises. Experiments show that our method can detect disguised faces in real time under the complex background and achieve acceptable disguised face recognition rate.","PeriodicalId":432938,"journal":{"name":"2014 IEEE Symposium on Computational Intelligence in Biometrics and Identity Management (CIBIM)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131466322","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 : 2014-12-01DOI: 10.1109/CIBIM.2014.7015444
C. Pagano, Eric Granger, R. Sabourin, A. Rattani, G. Marcialis, F. Roli
In many face recognition (FR) applications, changing capture conditions lead to divergence between facial models stored during enrollment and faces captured during operations. Moreover, it is often costly or infeasible to capture several high quality reference samples a priori to design representative facial models. Although self-updating models using high-confidence face captures appear promising, they raise several challenges when capture conditions change. In particular, face models of individuals may be corrupted by misclassified input captures, and their growth may require pruning to bound system complexity over time. This paper presents a system for self-update of facial models that exploits changes in capture conditions to assure the relevance of templates and to limit the growth of template galleries. The set of reference templates (facial model) of an individual is only updated to include new faces that are captured under significantly different conditions. In a particular implementation of this system, illumination changes are detected in order to select face captures from bio-login to be stored in a gallery. Face captures from a built-in still or video camera are taken at periodic intervals to authenticate the user having accessed a secured computer or network. Experimental results produced with the DIEE dataset show that the proposed system provides a comparable level of performance to the FR system that self-updates the gallery on all high-confidence face captures, but with significantly lower complexity, i.e., number of templates per individual.
{"title":"Efficient adaptive face recognition systems based on capture conditions","authors":"C. Pagano, Eric Granger, R. Sabourin, A. Rattani, G. Marcialis, F. Roli","doi":"10.1109/CIBIM.2014.7015444","DOIUrl":"https://doi.org/10.1109/CIBIM.2014.7015444","url":null,"abstract":"In many face recognition (FR) applications, changing capture conditions lead to divergence between facial models stored during enrollment and faces captured during operations. Moreover, it is often costly or infeasible to capture several high quality reference samples a priori to design representative facial models. Although self-updating models using high-confidence face captures appear promising, they raise several challenges when capture conditions change. In particular, face models of individuals may be corrupted by misclassified input captures, and their growth may require pruning to bound system complexity over time. This paper presents a system for self-update of facial models that exploits changes in capture conditions to assure the relevance of templates and to limit the growth of template galleries. The set of reference templates (facial model) of an individual is only updated to include new faces that are captured under significantly different conditions. In a particular implementation of this system, illumination changes are detected in order to select face captures from bio-login to be stored in a gallery. Face captures from a built-in still or video camera are taken at periodic intervals to authenticate the user having accessed a secured computer or network. Experimental results produced with the DIEE dataset show that the proposed system provides a comparable level of performance to the FR system that self-updates the gallery on all high-confidence face captures, but with significantly lower complexity, i.e., number of templates per individual.","PeriodicalId":432938,"journal":{"name":"2014 IEEE Symposium on Computational Intelligence in Biometrics and Identity Management (CIBIM)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132618348","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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