Header

UZH-Logo

Maintenance Infos

Learning to Recognize Familiar Faces in the Real World


Aryananda, L (2009). Learning to Recognize Familiar Faces in the Real World. In: IEEE International Conference on Robotics and Automation, Kobe, Japan, 12 May 2009 - 17 May 2009.

Abstract

We present an incremental and unsupervised face recognition system and evaluate it offline using data which were automatically collected by Mertz, a robotic platform embedded in real human environment. In an eight-day-long experiment, the robot autonomously detects, tracks, and segments face images during spontaneous interactions with over 500 passersby in public spaces and automatically generates a data set of over 100,000 face images. We describe and evaluate a novel face clustering algorithm using these data (without any manual processing) and also on an existing face recognition database. The face clustering algorithm yields good and robust performance despite the extremely noisy data segmented from the realistic and difficult public environment. In an incremental recognition scheme evaluation, the system is correct 74% of the time when it declares "I don't know this person" and 75.1% of the time when it declares " I know this person, he/she is ..." The latter accuracy improves to 83.8% if the system is allowed some learning curve delay in the beginning.

Abstract

We present an incremental and unsupervised face recognition system and evaluate it offline using data which were automatically collected by Mertz, a robotic platform embedded in real human environment. In an eight-day-long experiment, the robot autonomously detects, tracks, and segments face images during spontaneous interactions with over 500 passersby in public spaces and automatically generates a data set of over 100,000 face images. We describe and evaluate a novel face clustering algorithm using these data (without any manual processing) and also on an existing face recognition database. The face clustering algorithm yields good and robust performance despite the extremely noisy data segmented from the realistic and difficult public environment. In an incremental recognition scheme evaluation, the system is correct 74% of the time when it declares "I don't know this person" and 75.1% of the time when it declares " I know this person, he/she is ..." The latter accuracy improves to 83.8% if the system is allowed some learning curve delay in the beginning.

Statistics

Downloads

93 downloads since deposited on 16 Apr 2010
18 downloads since 12 months
Detailed statistics

Additional indexing

Item Type:Conference or Workshop Item (Paper), refereed, original work
Communities & Collections:03 Faculty of Economics > Department of Informatics
Dewey Decimal Classification:000 Computer science, knowledge & systems
Event End Date:17 May 2009
Deposited On:16 Apr 2010 05:15
Last Modified:11 Aug 2017 17:06
Additional Information:© 2009 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

Download

Preview Icon on Download
Preview
Filetype: PDF
Size: 1MB

TrendTerms

TrendTerms displays relevant terms of the abstract of this publication and related documents on a map. The terms and their relations were extracted from ZORA using word statistics. Their timelines are taken from ZORA as well. The bubble size of a term is proportional to the number of documents where the term occurs. Red, orange, yellow and green colors are used for terms that occur in the current document; red indicates high interlinkedness of a term with other terms, orange, yellow and green decreasing interlinkedness. Blue is used for terms that have a relation with the terms in this document, but occur in other documents.
You can navigate and zoom the map. Mouse-hovering a term displays its timeline, clicking it yields the associated documents.

Author Collaborations