一种基于混合多头交叉注意网络的面部抑郁识别方法
Yutong Li1, Zhenyu Liu1, Li Zhou1
1Gansu Provincial Key Laboratory of Wearable Computing, Lanzhou University, Lanzhou, China.
Frontiers in neuroscience
|June 9, 2023
概括
这项研究引入了混合多头交叉注意网络 (HMHN),以更准确地基于视频识别抑郁症. 这种新型深度学习模型有效地捕捉了面部特征的交互,优于现有的方法.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 生物医学工程 生物医学工程
背景情况:
- 卷积神经网络 (CNN) 在抑郁症分析中表现有前途,但由于空间局部性,难以学习长距离的面部特征依赖性.
- 单一注意力机制限制了模型同时关注多个关键面部区域的能力,以检测抑郁症.
研究的目的:
- 开发一种先进的深度学习框架,即混合多头交叉注意力网络 (HMHN),以克服目前基于CNN的抑郁症识别方面的局限性.
- 通过有效地整合来自不同面部区域的信息来提高基于视频的抑郁症分析的准确性和灵敏性.
主要方法:
- 该HMHN框架采用了两阶段的方法:电网智能关注 (GWA) 和深度特征融合 (DFF),用于低级特征提取.
- 第二阶段使用多头交叉注意力区块 (MAB) 和注意力融合区块 (AFB) 来编码本地特征之间的高阶交互以实现全球表示.
主要成果:
- 在AVEC2013和AVEC2014抑郁症数据集上的实验证明了HMHN的有效性.
- 拟议的方法实现了卓越的性能,超过了大多数基于视频的最先进的抑郁症识别方法,报告了RMSE和MAE值.
结论:
- HMHN模型成功地捕捉了来自多个面部区域的抑郁特征之间的高阶相互作用,显著减少了识别错误.
- 这种深度学习方法在抑郁症评估和诊断中具有很大的临床应用潜力.
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