Garuda: a deep learning based solution for capturing selfies safely

Jitender Singh Virk, Abhinav Dhall

Research output: Chapter in Book/Report/Conference proceedingConference PaperOtherpeer-review

2 Citations (Scopus)


Clicking selfies using mobile phones has become a trend in the past few years. It is documented that the thrill of clicking selfies at adventurous places has resulted in serious injuries and even death in some cases. To overcome this, we propose a system which can alert the user by detecting the level of danger in the background while capturing selfies. Our app is based on a deep Convolutional Neural Network (CNN). The prediction is performed as a 5 class classification problem with classes representing a different level of danger. Face detection and device orientation information are also used for robustness and lesser battery consumption.

Original languageEnglish
Title of host publicationProceedings of IUI 2019
EditorsOliver Brdiczka, Gaëlle Calvary, Polo Chau
Place of PublicationNew York NY USA
PublisherAssociation for Computing Machinery (ACM)
Number of pages2
ISBN (Electronic)9781450366731
Publication statusPublished - 16 Mar 2019
Externally publishedYes
EventInternational Conference on Intelligent User Interfaces 2019 - Los Angeles, United States of America
Duration: 16 Mar 201920 Mar 2019
Conference number: 24th

Publication series

NameInternational Conference on Intelligent User Interfaces, Proceedings IUI


ConferenceInternational Conference on Intelligent User Interfaces 2019
Abbreviated titleIUI 2019
Country/TerritoryUnited States of America
CityLos Angeles
Internet address


  • Deep learning
  • Safe selfie
  • Scene analysis
  • Selfie

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