Efficient Image Recognition and Retrieval on IoT-Assisted Energy-Constrained Platforms from Big Data Repositories

Irfan Mehmood, Amin Ullah, Khan Muhammad, Der-Jiunn Deng, Weizhi Meng, Fadi Al-Turjman, Muhammad Sajjad*, Victor Hugo C. de Albuquerque

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

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The advanced computational capabilities of many resource constrained devices such as smartphones have enabled various research areas including image retrieval from big data repositories for numerous IoT applications. The major challenges for image retrieval using smartphones in an IoT environment are the computational complexity and storage. To deal with big data in IoT environment for image retrieval, this paper proposes a light-weighted deep learning based system for energy-constrained devices. The system first detects and crops face regions from an image using Viola-Jones algorithm with additional face and non-face classifier to eliminate the miss-detection problem. Secondly, the system uses convolutional layers of a cost effective pre-trained CNN model with defined features to represent faces. Next, features of the big data repository are indexed to achieve a faster matching process for real-time retrieval. Finally, Euclidean distance is used to find similarity between query and repository images. For experimental evaluation, we created a local facial images dataset, including both single and group facial images. This dataset can be used by other researchers as a benchmark for comparison with other real-time facial image retrieval systems. The experimental results show that our proposed system outperforms other state-of-the-art feature extraction methods in terms of efficiency and retrieval for IoT-assisted energy-constrained platforms.
Original languageEnglish
JournalIEEE Internet of Things Journal
Issue number6
Pages (from-to)9246-9255
Number of pages11
Publication statusPublished - 2019


  • Image Retrieval
  • Internet of Things (IoT)
  • Big Data
  • Convolutional Neural Network
  • Energy-Constrained Platforms
  • Deep Learning.


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