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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.

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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:07 Dec 2017 02:04
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.

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