An approach to improving single sample face recognition using high confident tracking trajectories

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

3 Citations (Scopus)

Abstract

In this paper, single sample face recognition (SSFR) problem is addressed by introducing an adaptive biometric system within a modular architecture where one detector per target individual is proposed. For each detector, a face model is generated with the gallery face image and updated overtime. Sequential Karhunen-Loeve technique is applied to update the face model using representative face captures which are selected from the operational data by using reliable tracking trajectories. This process helps to induce intra-class variation of face appearance and improve representativeness of the face models. The effectiveness of the proposed method is detailed in security surveillance and user authentication using Chokepoint and FIA datasets in SSFR setting.

Original languageEnglish
Title of host publicationAdvances in Artificial Intelligence - 29th Canadian Conference on Artificial Intelligence, Canadian AI 2016, Proceedings
EditorsRichard Khoury, Christopher Drummond
Pages115-121
Number of pages7
DOIs
Publication statusPublished - 2016
Event29th Canadian Conference on Artificial Intelligence, AI 2016 - Victoria, Canada
Duration: 31 May 20163 Jun. 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9673
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference29th Canadian Conference on Artificial Intelligence, AI 2016
Country/TerritoryCanada
CityVictoria
Period31/05/163/06/16

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