東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Towards Automated Recognition of Bod...

Luo, Yu.

Linked to FindBook

Google Book

Amazon

博客來

Towards Automated Recognition of Bodily Expression of Emotion in the Wild.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Towards Automated Recognition of Bodily Expression of Emotion in the Wild./
Author:	Luo, Yu.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
Description:	127 p.
Notes:	Source: Dissertations Abstracts International, Volume: 83-03, Section: B.
Contained By:	Dissertations Abstracts International83-03B.
Subject:	Cameras. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28841647
ISBN:	9798460447480

Towards Automated Recognition of Bodily Expression of Emotion in the Wild.
Luo, Yu.

Towards Automated Recognition of Bodily Expression of Emotion in the Wild. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 127 p.

Source: Dissertations Abstracts International, Volume: 83-03, Section: B.

Thesis (Ph.D.)--The Pennsylvania State University, 2021.

This item must not be sold to any third party vendors.

Humans are arguably innately prepared to comprehend others' emotional expressions from subtle body movements. If robots or computers can be empowered with this capability, a number of robotic applications become possible. Automatically recognizing human bodily expression in unconstrained situations, however, is daunting given the incomplete understanding of the relationship between emotional expressions and body movements. The current research, as a multidisciplinary effort among computer and information sciences, psychology, and statistics, proposes a scalable and reliable crowdsourcing approach for collecting in-the-wild perceived emotion data for computers to learn to recognize body languages of humans. To accomplish this task, a large and growing annotated dataset with 9,876 video clips of body movements and 13,239 human characters, named BoLD (Body Language Dataset), has been created. Comprehensive statistical analysis of the dataset revealed many interesting insights. A system to model the emotional expressions based on bodily movements, named ARBEE (Automated Recognition of Bodily Expression of Emotion), has also been developed and evaluated. Our analysis shows the effectiveness of Laban Movement Analysis (LMA) features in characterizing arousal, and our experiments using LMA features further demonstrate computability of bodily expression. We report and compare results of several other baseline methods which were developed for action recognition based on two different modalities, body skeleton and raw image. The dataset and findings presented in this work will likely serve as a launchpad for future discoveries in body language understanding that will enable future robots to interact and collaborate more effectively with humans. Computationally representing human body movements from images is another aspect towards automated recognition of bodily expression. A fine-grained mesh of human pose and shape provides rich geometric information that enables many applications including bodily expression recognition. Estimating an accurate 3D human mesh from an image captured by a passive sensor is a highly challenging research problem. The mainstream approach, which uses deep learning, requires large-scale human pose/shape annotations in the training process. Currently, those annotations are mostly created from expensive indoor motion capture systems, thus both diversity and quantity are limited. We propose a new method to train a deep human mesh estimation model using a large quantity of unlabeled RGB-D images, which are inexpensive and convenient to collect. Depth information encoded in the data is used in the training process to achieve higher model accuracy. Our method is easy-to-implement and amenable to any other state-of-the-art parametric mesh modeling framework. We empirically demonstrate the effectiveness of this method based on real-world datasets, validating the value of the proposed ``learning from depth'' approach.

ISBN: 9798460447480Subjects--Topical Terms:

524039
Cameras.
Subjects--Index Terms:

Body language

Towards Automated Recognition of Bodily Expression of Emotion in the Wild.
LDR:04127nmm a2200361 4500 001 2283881
005 20211115071709.5
008 220723s2021 ||||||||||||||||| ||eng d
020 $a 9798460447480
035 $a (MiAaPQ)AAI28841647
035 $a (MiAaPQ)PennState_24462yzl5709
035 $a AAI28841647
040 $a MiAaPQ $c MiAaPQ
100 1 $a Luo, Yu. $3 1903375
245 1 0 $a Towards Automated Recognition of Bodily Expression of Emotion in the Wild.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2021
300 $a 127 p.
500 $a Source: Dissertations Abstracts International, Volume: 83-03, Section: B.
500 $a Advisor: Wang, James;Li, Jia.
502 $a Thesis (Ph.D.)--The Pennsylvania State University, 2021.
506 $a This item must not be sold to any third party vendors.
520 $a Humans are arguably innately prepared to comprehend others' emotional expressions from subtle body movements. If robots or computers can be empowered with this capability, a number of robotic applications become possible. Automatically recognizing human bodily expression in unconstrained situations, however, is daunting given the incomplete understanding of the relationship between emotional expressions and body movements. The current research, as a multidisciplinary effort among computer and information sciences, psychology, and statistics, proposes a scalable and reliable crowdsourcing approach for collecting in-the-wild perceived emotion data for computers to learn to recognize body languages of humans. To accomplish this task, a large and growing annotated dataset with 9,876 video clips of body movements and 13,239 human characters, named BoLD (Body Language Dataset), has been created. Comprehensive statistical analysis of the dataset revealed many interesting insights. A system to model the emotional expressions based on bodily movements, named ARBEE (Automated Recognition of Bodily Expression of Emotion), has also been developed and evaluated. Our analysis shows the effectiveness of Laban Movement Analysis (LMA) features in characterizing arousal, and our experiments using LMA features further demonstrate computability of bodily expression. We report and compare results of several other baseline methods which were developed for action recognition based on two different modalities, body skeleton and raw image. The dataset and findings presented in this work will likely serve as a launchpad for future discoveries in body language understanding that will enable future robots to interact and collaborate more effectively with humans. Computationally representing human body movements from images is another aspect towards automated recognition of bodily expression. A fine-grained mesh of human pose and shape provides rich geometric information that enables many applications including bodily expression recognition. Estimating an accurate 3D human mesh from an image captured by a passive sensor is a highly challenging research problem. The mainstream approach, which uses deep learning, requires large-scale human pose/shape annotations in the training process. Currently, those annotations are mostly created from expensive indoor motion capture systems, thus both diversity and quantity are limited. We propose a new method to train a deep human mesh estimation model using a large quantity of unlabeled RGB-D images, which are inexpensive and convenient to collect. Depth information encoded in the data is used in the training process to achieve higher model accuracy. Our method is easy-to-implement and amenable to any other state-of-the-art parametric mesh modeling framework. We empirically demonstrate the effectiveness of this method based on real-world datasets, validating the value of the proposed ``learning from depth'' approach.
590 $a School code: 0176.
650 4 $a Cameras. $3 524039
650 4 $a Human performance. $3 3562051
650 4 $a Data collection. $3 3561708
650 4 $a Emotions. $3 524569
650 4 $a Crowdsourcing. $3 3377825
650 4 $a Retouching. $3 3562950
650 4 $a Computer science. $3 523869
650 4 $a Artificial intelligence. $3 516317
650 4 $a Experimental psychology. $3 2144733
650 4 $a Information technology. $3 532993
653 $a Body language
653 $a Automated recognition
690 $a 0489
690 $a 0984
690 $a 0800
690 $a 0623
710 2 $a The Pennsylvania State University. $3 699896
773 0 $t Dissertations Abstracts International $g 83-03B.
790 $a 0176
791 $a Ph.D.
792 $a 2021
793 $a English
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28841647