An Empirical Approach for Automatic Face Clustering on Personal Lifelogging Images

An Empirical Approach for Automatic Face Clustering on Personal Lifelogging Images
Title:
An Empirical Approach for Automatic Face Clustering on Personal Lifelogging Images
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2017 IEEE 2nd International Conference on Signal and Image Processing
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Publication Date:
05 August 2017
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Abstract:
Life-logging applications generate a vast amount of personalized data that provides vital insights into the user’s daily life. One such key insight is the people whom the user has come across/interacted with during regular life. This can be obtained from the faces extracted from images acquired by a wearable life-logging camera. However, manual inspection and tagging of the life-logging images is cumbersome and highly subjective. Therefore, in this paper, a fully automatic method to extract and cluster the faces from the images obtained from a life-logging camera is designed and evaluated. It is shown that such a practical system designed using commercial off-the shelf devices and commercially available face recognition APIs is able to obtain human like precision, while the recall may be lower compared to human performance.
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PublisherCopyrights
Funding Info:
A*STAR JCO VIP REVIVE Project (1335h0009)
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(c) 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
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