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Analysis of Facial Emotion Recognition Technology and Its Effectiveness in Human Interaction

机译:面部情感识别技术分析及其对人类互动的效力

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摘要

Facial expressions are connected to experiencing emotions according to Facial-Feedback Theory of Emotion developed by Charles Darwin and William James [17]. Knowing how to read and interpret facial expressions helps us understand how human beings connect with one another in mobile-mediated communication (MMC) and improve our own abilities at managing emotions. There have been multiple types of research conducted that study and compare factors that influence facial emotion recognition in humans [10]. As one of the most prominent channel in human interactions, facial expression is a good indicator of a person's emotions. Recent technological advancements have had a drastic impact on human interaction. Besides face-to-face chat, video chatting become more and more popular in the digital age. This research provides to understand how video, voice and music influence emotion perception in the MMC context among different user groups. In this research, we evaluated challenges and effectiveness between two primary emotions: happiness and sadness in video chatting. This study analyzed the emotional influences of voice and music on video chatting and its effectiveness on sensing others emotional energy. Participants were from 18-42 years old and 98% of participants are in between 18-32 years old. A series of visual stimuli were created with four models were from Gen Z; Millennial; Gen X; and Baby Boomer. In addition, the research examines the comfort level with different relationships (e.g. friends; acquaintances; and strangers) in the video chatting environment.
机译:根据Charles Darwin和William James [17]的面部反馈理论,面部表情与体育情绪有关的情感。了解如何阅读和解释面部表情有助于我们了解人类如何在移动式沟通(MMC)中彼此相互联系,并提高我们在管理情绪方面的能力。已经有多种类型的研究进行了研究和比较人类面部情感识别的因素[10]。作为人类互动中最突出的渠道之一,面部表情是一个人情绪的良好指标。最近的技术进步对人类互动产生了急剧影响。除了面对面的聊天之外,视频聊天在数字时代变得越来越受欢迎。本研究提供了了解视频,语音和音乐如何影响不同用户组中MMC上下文中的情绪感知。在这项研究中,我们评估了两种主要情绪之间的挑战和有效性:视频聊天的幸福和悲伤。本研究分析了语音与音乐对视频聊天的情感影响及其对别人情感能量的效力。参与者从18-42岁以下,98%的参与者在18-32岁之间。采用四种模型创建了一系列视觉刺激;千禧一代; Gen X;和婴儿潮一代。此外,该研究还研究了不同关系的舒适度(例如朋友;熟人;熟人;陌生人)在视频聊天环境中。

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