Pub Date : 2016-02-01DOI: 10.1109/ISBA.2016.7477246
N. Almudhahka, M. Nixon, Jonathon S. Hare
Soft biometrics enable the identification of subjects based on semantic descriptions collected from eye-witnesses allowing people to search in surveillance databases. Although research has recently shown an increased interest in soft biometrics, not much of the work have used crowdsourcing, and it did not investigate the impact of feature selection on identification. In this paper, we introduce a new set of facial soft biometrics and labels with a novel description for the eyebrow region. Also, we examine the use of crowdsourcing for labelling the comparative facial soft biometrics and assess its impact on the identification. Moreover, we explore the impact of feature selection with our biometric measures and evaluate the effect of label scale compression. Experiments based on the Southampton biometric tunnel database demonstrate a 100% rank-1 identification rate using 20 features only.
{"title":"Human face identification via comparative soft biometrics","authors":"N. Almudhahka, M. Nixon, Jonathon S. Hare","doi":"10.1109/ISBA.2016.7477246","DOIUrl":"https://doi.org/10.1109/ISBA.2016.7477246","url":null,"abstract":"Soft biometrics enable the identification of subjects based on semantic descriptions collected from eye-witnesses allowing people to search in surveillance databases. Although research has recently shown an increased interest in soft biometrics, not much of the work have used crowdsourcing, and it did not investigate the impact of feature selection on identification. In this paper, we introduce a new set of facial soft biometrics and labels with a novel description for the eyebrow region. Also, we examine the use of crowdsourcing for labelling the comparative facial soft biometrics and assess its impact on the identification. Moreover, we explore the impact of feature selection with our biometric measures and evaluate the effect of label scale compression. Experiments based on the Southampton biometric tunnel database demonstrate a 100% rank-1 identification rate using 20 features only.","PeriodicalId":198009,"journal":{"name":"2016 IEEE International Conference on Identity, Security and Behavior Analysis (ISBA)","volume":"84 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122966615","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}
Recently, handprint-based recognition system has been widely applied for security and surveillance purposes. The success of this technology has also demonstrated that handprint is a good approach to perform forensic identification. However, existing identification systems are nearly based on the handprints that could be easily prevented. In contrast to earlier works, we exploit the thermal handprint and introduce a novel distance metric for thermal handprint dissimilarity measure, called Heat-Earth Mover's Distance (HEMD). The HEMD is designed to classify heat-based handprints that can be obtained even when the subject wears a glove. HEMD can effectively recognize the subjects by computing the distance between point distributions of target and training handprints. Through a comprehensive study, our identification system demonstrates the performance even with the handprints obtained by the subject wearing a glove. With 20 subjects, our proposed system achieves an accuracy of 94.13%for regular handprints and 92.00% for handprints produced with latex gloves.
{"title":"Thermal handprint analysis for forensic identification using Heat-Earth Mover's Distance","authors":"Kun Woo Cho, Feng Lin, Chen Song, Xiaowei Xu, Fuxing Gu, Wenyao Xu","doi":"10.1109/ISBA.2016.7477241","DOIUrl":"https://doi.org/10.1109/ISBA.2016.7477241","url":null,"abstract":"Recently, handprint-based recognition system has been widely applied for security and surveillance purposes. The success of this technology has also demonstrated that handprint is a good approach to perform forensic identification. However, existing identification systems are nearly based on the handprints that could be easily prevented. In contrast to earlier works, we exploit the thermal handprint and introduce a novel distance metric for thermal handprint dissimilarity measure, called Heat-Earth Mover's Distance (HEMD). The HEMD is designed to classify heat-based handprints that can be obtained even when the subject wears a glove. HEMD can effectively recognize the subjects by computing the distance between point distributions of target and training handprints. Through a comprehensive study, our identification system demonstrates the performance even with the handprints obtained by the subject wearing a glove. With 20 subjects, our proposed system achieves an accuracy of 94.13%for regular handprints and 92.00% for handprints produced with latex gloves.","PeriodicalId":198009,"journal":{"name":"2016 IEEE International Conference on Identity, Security and Behavior Analysis (ISBA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130598880","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 : 2016-02-01DOI: 10.1109/ISBA.2016.7477233
Mateusz Trokielewicz
This paper delivers a new database of iris images collected in visible light using a mobile phone's camera and presents results of experiments involving existing commercial and open-source iris recognition methods, namely: Iri-Core, VeriEye, MIRLIN and OSIRIS. Several important observations are made. First, we manage to show that after simple preprocessing, such images offer good visibility of iris texture even in heavily-pigmented irides. Second, for all four methods, the enrollment stage is not much affected by the fact that different type of data is used as input. This translates to zero or close-to-zero Failure To Enroll, i.e., cases when templates could not be extracted from the samples. Third, we achieved good matching accuracy, with correct genuine match rate exceeding 94.5% for all four methods, while simultaneously being able to maintain zero false match rate in every case. Correct genuine match rate of over 99.5% was achieved using one of the commercial methods, showing that such images can be used with the existing biometric solutions with minimum additional effort required. Finally, the experiments revealed that incorrect image segmentation is the most prevalent cause of recognition accuracy decrease. To our best knowledge, this is the first database of iris images captured using a mobile device, in which image quality exceeds this of a near-infrared illuminated iris images, as defined in ISO/IEC 19794-6 and 29794-6 documents. This database will be publicly available to all researchers.
{"title":"Iris recognition with a database of iris images obtained in visible light using smartphone camera","authors":"Mateusz Trokielewicz","doi":"10.1109/ISBA.2016.7477233","DOIUrl":"https://doi.org/10.1109/ISBA.2016.7477233","url":null,"abstract":"This paper delivers a new database of iris images collected in visible light using a mobile phone's camera and presents results of experiments involving existing commercial and open-source iris recognition methods, namely: Iri-Core, VeriEye, MIRLIN and OSIRIS. Several important observations are made. First, we manage to show that after simple preprocessing, such images offer good visibility of iris texture even in heavily-pigmented irides. Second, for all four methods, the enrollment stage is not much affected by the fact that different type of data is used as input. This translates to zero or close-to-zero Failure To Enroll, i.e., cases when templates could not be extracted from the samples. Third, we achieved good matching accuracy, with correct genuine match rate exceeding 94.5% for all four methods, while simultaneously being able to maintain zero false match rate in every case. Correct genuine match rate of over 99.5% was achieved using one of the commercial methods, showing that such images can be used with the existing biometric solutions with minimum additional effort required. Finally, the experiments revealed that incorrect image segmentation is the most prevalent cause of recognition accuracy decrease. To our best knowledge, this is the first database of iris images captured using a mobile device, in which image quality exceeds this of a near-infrared illuminated iris images, as defined in ISO/IEC 19794-6 and 29794-6 documents. This database will be publicly available to all researchers.","PeriodicalId":198009,"journal":{"name":"2016 IEEE International Conference on Identity, Security and Behavior Analysis (ISBA)","volume":"319 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116231712","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 : 2016-02-01DOI: 10.1109/ISBA.2016.7477247
J. Smereka, B. Kumar, Andres Rodriguez
A fundamental step in biometric recognition is to identify discriminative features in order to maximize user separation. Matching systems will often require manually choosing these discriminative regions of interest for feature extraction and/or score fusion. Specifically within periocular recognition scenarios, previous works segment the eyebrow and/or eye. While such efforts demonstrate the discriminative power of these regions, in this paper we show that there are various scenarios where blindly employing this type of segmentation is not consistently effective. Thus, we introduce a novel unsupervised approach to automatically select regions in the periocular image for improved match performance. A periocular image is segmented into rectangular regions (this process is referred to as patch segmentation) which improve the overall discrimination ability of the bio-metric samples being matched. We demonstrate the efficacy of this approach via extensive numerical results on multiple periocular biometric databases exhibiting challenges commonly found in uncontrolled acquisition environments. As the proposed approach is shown to be equivalent to or better than state-of-the-art on each dataset, our results indicate that our patch segmentation is an important step which can greatly influence system performance.
{"title":"Selecting discriminative regions for periocular verification","authors":"J. Smereka, B. Kumar, Andres Rodriguez","doi":"10.1109/ISBA.2016.7477247","DOIUrl":"https://doi.org/10.1109/ISBA.2016.7477247","url":null,"abstract":"A fundamental step in biometric recognition is to identify discriminative features in order to maximize user separation. Matching systems will often require manually choosing these discriminative regions of interest for feature extraction and/or score fusion. Specifically within periocular recognition scenarios, previous works segment the eyebrow and/or eye. While such efforts demonstrate the discriminative power of these regions, in this paper we show that there are various scenarios where blindly employing this type of segmentation is not consistently effective. Thus, we introduce a novel unsupervised approach to automatically select regions in the periocular image for improved match performance. A periocular image is segmented into rectangular regions (this process is referred to as patch segmentation) which improve the overall discrimination ability of the bio-metric samples being matched. We demonstrate the efficacy of this approach via extensive numerical results on multiple periocular biometric databases exhibiting challenges commonly found in uncontrolled acquisition environments. As the proposed approach is shown to be equivalent to or better than state-of-the-art on each dataset, our results indicate that our patch segmentation is an important step which can greatly influence system performance.","PeriodicalId":198009,"journal":{"name":"2016 IEEE International Conference on Identity, Security and Behavior Analysis (ISBA)","volume":"420 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124207924","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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