Reduced residual nets (red-nets): Low powered adversarial outlier detectors

Saad Sadiq, Marina Zmieva, Mei-Ling Shyu, Shu Ching Chen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Scopus citations

Abstract

The evolution of information science has seen an immense growth in multimedia data, specially in the case of CCTV live stream capture. The tremendously large volumes of multimedia data give rise to a particularly challenging problem called the outlier events of interest detection. In the wake of growing school shootings in the United States, there needs to be a rethinking of our security strategies regarding the safety of children at school utilizing multimedia data mining research. This paper proposes a novel method to identify faces of interest using live stream CCTV data. By integrating the adversarial information, the proposed framework can help imbalance facial recognition and enhance rare class mining even with trivial scores from the minority class. Experimental results on the Faces in the Wile (FIW) dataset demonstrate the effectiveness of the proposed framework with promising performance. The proposed method was implemented on a low powered NVIDIA TX2 for real-time face recognition. The proposed framework was benchmarked against several existing state-of-the-art methods for accuracy, computational complexity, and real-time power measurement. The proposed method performs very well under the power and complexity constraints.

Original languageEnglish (US)
Title of host publicationProceedings - 2018 IEEE 19th International Conference on Information Reuse and Integration for Data Science, IRI 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages436-443
Number of pages8
ISBN (Print)9781538626597
DOIs
StatePublished - Aug 2 2018
Event19th IEEE International Conference on Information Reuse and Integration for Data Science, IRI 2018 - Salt Lake City, United States
Duration: Jul 7 2018Jul 9 2018

Other

Other19th IEEE International Conference on Information Reuse and Integration for Data Science, IRI 2018
CountryUnited States
CitySalt Lake City
Period7/7/187/9/18

Keywords

  • Adversarial outlier detection
  • Face recognition
  • Low powered
  • Rare class mining
  • Real-time multimedia

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Software
  • Artificial Intelligence
  • Information Systems and Management
  • Safety, Risk, Reliability and Quality
  • Public Administration

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    Sadiq, S., Zmieva, M., Shyu, M-L., & Chen, S. C. (2018). Reduced residual nets (red-nets): Low powered adversarial outlier detectors. In Proceedings - 2018 IEEE 19th International Conference on Information Reuse and Integration for Data Science, IRI 2018 (pp. 436-443). [8424741] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IRI.2018.00070