A Continuous Learning System for Face Clustering and Recognition

Siffi Singh, Hsueh Ming Hang

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

Abstract

This work is to design a high performance and light weight system that can be trained and work well on practical real- world face images. It can run on the mobile phones in the market, and it can automatically identify and group photos in a personal digital album. One target of the research is to produce a system that can be given in the hands of users for long-term. To achieve this goal, we employ the face recognition and object clustering techniques to build a system that can update the number of classes (faces) with a continuously growing number of input photos. This system can be used together with the personal storage (such as hard disk drives); therefore, it has the advantage of privacy. Hence, we propose a system pipeline to create a smart album that will automatically cluster photos and recognize previously seen faces when the user is adding pictures to it. We have done experiments on the public and self-collected datasets and the system is able to perform well even on the challenging images that are difficult to cluster by using the conventional techniques.

Original languageEnglish
Title of host publication2021 IEEE International Conference on Consumer Electronics, ICCE 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728197661
DOIs
StatePublished - 10 Jan 2021
Event2021 IEEE International Conference on Consumer Electronics, ICCE 2021 - Las Vegas, United States
Duration: 10 Jan 202112 Jan 2021

Publication series

NameDigest of Technical Papers - IEEE International Conference on Consumer Electronics
Volume2021-January
ISSN (Print)0747-668X

Conference

Conference2021 IEEE International Conference on Consumer Electronics, ICCE 2021
CountryUnited States
CityLas Vegas
Period10/01/2112/01/21

Keywords

  • Deep Learning
  • Face Clustering
  • Face Recognition

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