gb2s μ MOD: A MUltiMODal biometric video database using visible and IR light

Rios-Sanchez, Belen; Arriaga-Gómez, Miguel F.; Guerra-Casanova, Javier; Santos-Sierra, Daniel de; Mendizábal-Vázquez, Ignacio de; Bailador, Gonzalo; Ávila, Carmen Sánchez

doi:10.1016/j.inffus.2015.09.003

Cited by 12 publications

(12 citation statements)

References 24 publications

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“…Accordingly, the images present different degrees of difficulty regarding to pose, scale, background, lighting conditions, appearance, accessories and expressions. In particular, BioID ( [24]), EUCFI ([25]), ORL ( [26]), Extended Yale B ( [27]), Print-Attack ( [28]) and gb2sµMOD_Face_Dataset ( [29]) were used together with three proprietary datasets:…”

Section: Databasesmentioning

confidence: 99%

Deep Learning for Facial Recognition on Single Sample per Person Scenarios with Varied Capturing Conditions

et al. 2019

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Single sample per person verification has received considerable attention because of its relevance in security, surveillance and border crossing applications. Nowadays, e-voting and bank of the future solutions also join this scenario, opening this field of research to mobile and low resources devices. These scenarios are characterised by the availability of a single image during the enrolment of the users into the system, so they require a solution able to extract knowledge from previous experiences and similar environments. In this study, two deep learning models for face recognition, which were specially designed for applications on mobile devices and resources saving environments, were described and evaluated together with two publicly available models. This evaluation aimed not only to provide a fair comparison between the models but also to measure to what extent a progressive reduction of the model size influences the obtained results.The models were assessed in terms of accuracy and size with the aim of providing a detailed evaluation which covers as many environmental conditions and application requirements as possible. To this end, a well-defined evaluation protocol and a great number of varied databases, public and private, were used.

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Section: Databasesmentioning

confidence: 99%

Deep Learning for Facial Recognition on Single Sample per Person Scenarios with Varied Capturing Conditions

et al. 2019

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“…Towards this objective, MCS approaches can be instrumental for analyzing the increasing amount of multimodal biometric data available nowadays [51,52]. MCS methods can be quite helpful to analyze those data as they permit to simultaneously analyse and model complex yet structured relations on heterogenous data [53], which is the case in biometrics, e.g.…”

Section: Challenges In Biometrics: Role Of Mcsmentioning

confidence: 99%

Multiple classifiers in biometrics. Part 2: Trends and challenges

et al. 2018

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“…In this method, individual conformity assessments are combined after normalization. This method is most commonly used [59,60,61] while it provides clear and simple results processing.…”

Section: System Descriptionmentioning

confidence: 99%

Multi-Biometric System Based on Cutting-Edge Equipment for Experimental Contactless Verification

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Krejcar

Selamat

et al. 2019

Sensors

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Biometric verification methods have gained significant popularity in recent times, which has brought about their extensive usage. In light of theoretical evidence surrounding the development of biometric verification, we proposed an experimental multi-biometric system for laboratory testing. First, the proposed system was designed such that it was able to identify and verify a user through the hand contour, and blood flow (blood stream) at the upper part of the hand. Next, we detailed the hard and software solutions for the system. A total of 40 subjects agreed to be a part of data generation team, which produced 280 hand images. The core of this paper lies in evaluating individual metrics, which are functions of frequency comparison of the double type faults with the EER (Equal Error Rate) values. The lowest value was measured for the case of the modified Hausdorff distance metric - Maximally Helicity Violating (MHV). Furthermore, for the verified biometric characteristics (Hamming distance and MHV), appropriate and suitable metrics have been proposed and experimented to optimize system precision. Thus, the EER value for the designed multi-biometric system in the context of this work was found to be 5%, which proves that metrics consolidation increases the precision of the multi-biometric system. Algorithms used for the proposed multi-biometric device shows that the individual metrics exhibit significant accuracy but perform better on consolidation, with a few shortcomings.

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gb2s μ MOD: A MUltiMODal biometric video database using visible and IR light

Cited by 12 publications

References 24 publications

Deep Learning for Facial Recognition on Single Sample per Person Scenarios with Varied Capturing Conditions

Deep Learning for Facial Recognition on Single Sample per Person Scenarios with Varied Capturing Conditions

Multiple classifiers in biometrics. Part 2: Trends and challenges

Multi-Biometric System Based on Cutting-Edge Equipment for Experimental Contactless Verification

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