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16.05 - High Dimensional Data Reduction, Sampling and Visualization for Big Data Applications

Project - Summary


In this project, we aim to overcome the limitation and shortcoming of current visualization, data analysis, data sampling techniques to make sense of complex big data through latent theme extraction, to detect emerging practices, recommendation, and collaborative filtering.


  • We Develop a unifying platform for data dimensional reduction, data 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 a real-world setting.

Project - Team

Team Member Role Email Phone Number Academic Site/IAB
Moncef Gabbouj PI +358 (400) 736613 Tampere University
Alexandros Iosifidis Co-PI Not available Not available Tampere University
Honglei Zhang Researcher Not available Not available Tampere University
Muhammad Adeel Waris Researcher Not available Not available Tampere University

Project - Impact and Uses/Benefits

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 area. Accurate and fast recognition, surveillance, 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.

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.

Project - Deep Dive

Project - Documents

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projects/year5/16.05.1566397009.txt.gz · Last modified: 2019/08/21 09:16 by sally.johnson