Face Detection and Tagging Using Deep Learning

Jinesh Mehta, Eshaan Ramnani, Sanjay Singh

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

20 Citations (Scopus)

Abstract

With the social media boom in today's world, we see people constantly uploading photos of themselves along with their friends and family on various social media platforms such as Facebook, Instagram, Twitter, Google+, etc. What if they want to see all the photos in a categorized form such as photos with a particular person. In this paper, we extend the concept of Multiview Face Detection using Convolution Neural Networks (CNN) used by Farfade et al. by providing a tagging system for the detected faces. For the face detection, we use Deep Dense Face Detector, which uses a single model based on deep convolutional neural networks. All the detected faces are recognized using Local Binary Patterns Histograms (LBPH) method. Precision, recall, and F-measure are the parameters used to measure the performance of the algorithm. An accuracy of 85% is achieved for tagging the faces which are successfully detected.

Original languageEnglish
Title of host publication2nd International Conference on Computer, Communication, and Signal Processing
Subtitle of host publicationSpecial Focus on Technology and Innovation for Smart Environment, ICCCSP 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Print)9781538611418
DOIs
Publication statusPublished - 30-08-2018
Event2nd International Conference on Computer, Communication, and Signal Processing, ICCCSP 2018 - Chennai, India
Duration: 22-02-201823-02-2018

Publication series

Name2nd International Conference on Computer, Communication, and Signal Processing: Special Focus on Technology and Innovation for Smart Environment, ICCCSP 2018

Conference

Conference2nd International Conference on Computer, Communication, and Signal Processing, ICCCSP 2018
Country/TerritoryIndia
CityChennai
Period22-02-1823-02-18

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Hardware and Architecture
  • Signal Processing
  • Instrumentation

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