AM-FED+: An Extended Dataset of Naturalistic Facial Expressions Collected in Everyday Settings
Public datasets have played a significant role in advancing the state-of-the-art in automated facial coding. Many of these datasets contain posed expressions and/or videos recorded in controlled lab conditions with little variation in lighting or head pose. As such, the data do not reflect the condi...
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Veröffentlicht in: | IEEE transactions on affective computing 2019-01, Vol.10 (1), p.7-17 |
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creator | McDuff, Daniel Amr, May Kaliouby, Rana el |
description | Public datasets have played a significant role in advancing the state-of-the-art in automated facial coding. Many of these datasets contain posed expressions and/or videos recorded in controlled lab conditions with little variation in lighting or head pose. As such, the data do not reflect the conditions observed in many real-world applications. We present AM-FED+ an extended dataset of naturalistic facial response videos collected in everyday settings. The dataset contains 1,044 videos of which 545 videos (263,705 frames or 21,859 seconds) have been comprehensively manually coded for facial action units. These videos act as a challenging benchmark for automated facial coding systems. All the videos contain gender labels and a large subset (77 percent) contain age and country information. Subject self-reported liking and familiarity with the stimuli are also included. We provide automated facial landmark detection locations for the videos. Finally, baseline action unit classification results are presented for the coded videos. The dataset is available to download online: https://www.affectiva.com/facial-expression-dataset/ |
doi_str_mv | 10.1109/TAFFC.2018.2801311 |
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Many of these datasets contain posed expressions and/or videos recorded in controlled lab conditions with little variation in lighting or head pose. As such, the data do not reflect the conditions observed in many real-world applications. We present AM-FED+ an extended dataset of naturalistic facial response videos collected in everyday settings. The dataset contains 1,044 videos of which 545 videos (263,705 frames or 21,859 seconds) have been comprehensively manually coded for facial action units. These videos act as a challenging benchmark for automated facial coding systems. All the videos contain gender labels and a large subset (77 percent) contain age and country information. Subject self-reported liking and familiarity with the stimuli are also included. We provide automated facial landmark detection locations for the videos. Finally, baseline action unit classification results are presented for the coded videos. 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Many of these datasets contain posed expressions and/or videos recorded in controlled lab conditions with little variation in lighting or head pose. As such, the data do not reflect the conditions observed in many real-world applications. We present AM-FED+ an extended dataset of naturalistic facial response videos collected in everyday settings. The dataset contains 1,044 videos of which 545 videos (263,705 frames or 21,859 seconds) have been comprehensively manually coded for facial action units. These videos act as a challenging benchmark for automated facial coding systems. All the videos contain gender labels and a large subset (77 percent) contain age and country information. Subject self-reported liking and familiarity with the stimuli are also included. We provide automated facial landmark detection locations for the videos. Finally, baseline action unit classification results are presented for the coded videos. The dataset is available to download online: https://www.affectiva.com/facial-expression-dataset/</description><subject>Automation</subject><subject>Coding</subject><subject>corpora</subject><subject>dataset</subject><subject>Datasets</subject><subject>Downloading</subject><subject>Encoding</subject><subject>Face recognition</subject><subject>facial action coding system</subject><subject>Facial expressions</subject><subject>Lighting</subject><subject>Task analysis</subject><subject>Training</subject><subject>Videos</subject><issn>1949-3045</issn><issn>1949-3045</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNpNkE1LAzEQhoMoWLR_QC8Bj7I1X_sRb0vbVaHqwXo1ZLOzkrLu1iQV--9NbRHnMnN4n3fgQeiCkgmlRN4sy6qaThihxYQVhHJKj9CISiETTkR6_O8-RWPvVyQO5zxj-Qi9lY9JNZ9d3-Kyx_PvAH0DDZ7poD0EPLT4SYeN0531wRpcaWN1F3NrB97bofd4OnQdmBAhGwu-wG0bvcUvEILt3_05Oml152F82GfotZovp_fJ4vnuYVouEsMoCQkTXLNcZ6KFpiZc1DUVpJGE1GAyyZuM06yVHIzQjENjSMZlmuapyNsiJVrzM3S171274XMDPqjVsHF9fKkYlZSmJGVZTLF9yrjBewetWjv7od1WUaJ2KtWvSrVTqQ4qI3S5hywA_AEFy6WIEn8Aygxt3Q</recordid><startdate>201901</startdate><enddate>201901</enddate><creator>McDuff, Daniel</creator><creator>Amr, May</creator><creator>Kaliouby, Rana el</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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Many of these datasets contain posed expressions and/or videos recorded in controlled lab conditions with little variation in lighting or head pose. As such, the data do not reflect the conditions observed in many real-world applications. We present AM-FED+ an extended dataset of naturalistic facial response videos collected in everyday settings. The dataset contains 1,044 videos of which 545 videos (263,705 frames or 21,859 seconds) have been comprehensively manually coded for facial action units. These videos act as a challenging benchmark for automated facial coding systems. All the videos contain gender labels and a large subset (77 percent) contain age and country information. Subject self-reported liking and familiarity with the stimuli are also included. We provide automated facial landmark detection locations for the videos. Finally, baseline action unit classification results are presented for the coded videos. 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subjects | Automation Coding corpora dataset Datasets Downloading Encoding Face recognition facial action coding system Facial expressions Lighting Task analysis Training Videos |
title | AM-FED+: An Extended Dataset of Naturalistic Facial Expressions Collected in Everyday Settings |
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