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projects:year5:16.05 [2019/08/21 09:14]
sally.johnson created
projects:year5:16.05 [2019/08/22 10:48] (current)
sally.johnson [Project - Documents]
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 ===== Project - Summary ===== ===== Project - Summary =====
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-The use of face verification systems as a primary source of authenization has been common over the past few years. Despite recent advances in face recognition systems, there are still many open breaches left in this domain. One of the practical challenges is to secure face biometric systems from intruder's attackswhere an unauthorized person tries to gain access by presenting a counterfeit (images/videos) to the face biometric system. We proposed a novel approachwhich can be easily integrated to existing face verification systems without any additional hardware deployment. Systems that deliver the power to authenticate persons accurately,swiftlyreliably, without invading privacy, cost effectively, in a user-friendly manner and without requiring radical modifications to the existing infrastructures are desiredThis field of detection of imposter attempts is an open research problemas more sophisticated and advanced spoofing attempts come into play. Current anti-spoofing methods suffer from various problems that make them unreliable and inadequate to integrate them with face recognition systems. We approach this problem within research scenarios over the distinct classification schemes: Learning with large multi-targets Convolutional Neural Network (CNN) that simultaneously recorgnizes faces and detects counterfeit attacks.+**Objectives:** 
 +In this projectwe aim to overcome the limitation and shortcoming of current visualization, data analysisdata sampling techniques to make sense of complex big data through latent theme extraction, to detect emerging practicesrecommendation, and collaborative filtering  
 +  * We Develop a unifying platform for data dimensional reductiondata sampling and visualizing various complex high dimensional big data, Develop analytical and visualization tools and solutions for various real-world applications  
 +  * Worked with IAB members to evaluate the prototype systems in real-world setting. 
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 ===== Project - Team ===== ===== Project - Team =====
-^ Team Member           ^ Role        ^ Email                   ^ Phone Number       ^ Academic Site/IAB   +^ Team Member      ^ Role              ^ Email          ^ Phone Number   ^ Academic Site/IAB  
-Moncef Gabbouj        | PI          |  | +358 (400) 736613  | Tampere University +Xiaohua Tony Hu  | PI                | Not available  | Not available  Drexel University 
-| Alexandros Iosifidis  Co-PI       | Not available           | Not available      Tampere University +Xiaoli Song      Graduate Student  | Not available  | Not available Drexel University  |
-Honglei Zhang         Researcher  | Not available           | Not available      | Tampere University  | +
-| Muhammad Adeel Waris  | Researcher  | Not available           | Not available      | Tampere University  |+
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 ===== Project - Impact and Uses/Benefits ===== ===== Project - Impact and Uses/Benefits =====
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-The use of face verification systems as a primary source of authentication has been very common over the past few years. Despite the advance in face recognition systems, there are still many open problems in this areaAccurate and fast recognitionsurveillance, learning and spoofing detection in large face databases with the cheapest possible way are essential to most industry sectors to maximize their security, revenues and competitiveness. Our research done in this project proved that the combined system can be easily adapted by industry. Our industry partners have established projects to integrate the system into their own and will showcase the integrated system in an important national event. +We evaluated the proposed robust dimensional reduction, sampling and visualization with high level of performance in purpose of generating useful knowledge in big data applications on social mediaHoweverour model can be applied to a wide variety of problems 
- +
-Our reserach also shows the limitation of spoofing detection using noise patterns and presents the future direction of providing reliable spoofing attack detection systems. We have also shown the limitations of the proposed systems in partially occulded faces and in the case of training with a few samples. Such limitations will be mitigated in the next 2017-2018 CVDI project.+
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 ===== Project - Documents ===== ===== Project - Documents =====
projects/year5/16.05.1566396857.txt.gz · Last modified: 2019/08/21 09:14 by sally.johnson