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利用面部情绪识别系统用于环境辅助生活技术,通过解释用户的情绪状态来触发

Samuele Russo¹, Imad Eddine Tibermacine², Cristian Randieri³

¹Department of Psychology, Sapienza University of Rome, Rome, Italy.

Frontiers in neuroscience

|September 15, 2025

概括

这项研究引入了一种新的面部情感识别 (FER) 系统,使用空间变压器网络和多重自我注意力来提高准确性. 该系统将识别的情绪转化为实时环境照明线索,以提高智能环境中的用户舒适度.

科学领域:

计算机科学计算机科学
人工智能的人工智能
人与计算机的交互

背景情况:

面部情绪识别 (FER) 对适应性智能环境和机器人技术至关重要.
将情绪转化为环境线索,如彩色照明,可以在环境辅助生活 (AAL) 设置中增强用户体验.

研究的目的:

开发一个精确识别情绪并将其转化为实时环境照明的FER管道.
通过影响适应性系统,提高用户的舒适性和参与AAL设置.

主要方法:

一个新的FER管道结合了空间变压器网络 (STN) 进行姿势不变聚焦和多重自我注意 (MSA) 块.
用MSA块增强的VGG风格的骨干,在FER+数据集上进行训练,使用加权抽样来解决阶级不平衡.
软max概率与Plutchik轮衍生的色调的线性混合,以实时控制RGB照明.

主要成果:

VGGFac-STN-MSA模型在FER+上实现了82.54%的测试准确度,超过了基线模型.
废弃性研究证实,MSA阻断有助于+1%的准确性.
连续的色彩混合在概念验证中展示了光线的平滑,强度意识的过渡.

结论:

拟议的注意力方案是无关架构的,计算效率高,并且显著提高了FER准确性,特别是在低分辨率面部.
直接将情感概率分布与RGB光谱结合,为影响适应性AAL系统提供了一个感知上有意义的道.

关键词:

环境辅助生活环境辅助生活面部情绪识别面部情绪识别人与机器人的交互自己注意力自我注意力空间变压器网络的空间变压器网络

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