Depression Intensity Recognition Based on Perceptually Locally-enhanced Global Depression Features and Fused Global-local Semantic Correlation Features on Faces
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摘要: 现有基于深度学习的大多数方法在实现患者抑郁程度自动识别的过程中，主要存在两大挑战：(1)难以利用深度模型自动地从面部表情有效学习到抑郁强度相关的全局上下文信息，(2)往往忽略抑郁强度相关的全局和局部信息之间的语义一致性。为此，该文提出一种全局抑郁特征局部感知力增强和全局-局部语义相关性特征融合(PLEGDF-FGLSCF)的抑郁强度识别深度模型。首先，设计了全局抑郁特征局部感知力增强(PLEGDF)模块，用于提取面部局部区域之间的语义相关性信息，促进不同局部区域与抑郁相关的信息之间的交互，从而增强局部抑郁特征驱动的全局抑郁特征表达力。然后，提出了全局-局部语义相关性特征融合(FGLSCF)模块，用于捕捉全局和局部语义信息之间的关联性，实现全局和局部抑郁特征之间的语义一致性描述。最后，在AVEC2013和AVEC2014数据集上，利用PLEGDF-FGLSCF模型获得的识别结果在均方根误差(RMSE)和平均绝对误差(MAE)指标上的值分别是7.75/5.96和7.49/5.99，优于大多数已有的基准模型，证实了该方法的合理性和有效性。Abstract: For automatic recognition of the depression intensity in patients, the existing deep learning based methods typically face two main challenges: (1) It is difficult for deep models to effectively capture the global context information relevant to the level of depression intensity from facial expressions, and (2) the semantic consistency between the global semantic information and the local one associated with depression intensity is often ignored. One new deep neural network for recognizing the severity of depressive symptoms, by combining the Perceptually Locally-Enhanced Global Depression Features and the Fused Global-Local Semantic Correlation Features (PLEGDF-FGLSCF), is proposed in this paper. Firstly, the PLEGDF module for the extraction of global depression features with local perceptual enhancement, is designed to extract the semantic correlations among local facial regions, to promote the interactions between depression-relevant information in different local regions, and thus to enhance the expressiveness of the global depression features driven by the local ones. Secondly, in order to achieve full integration of global and local semantic features related to depression severity, the FGLSCF module is proposed, aiming to capture the correlation of global and local semantic information and thus to ensure the semantic consistency in describing the depression intensity by means of global and local semantic features. Finally, on the AVEC2013 and AVEC2014 datasets, the PLEGDF-FGLSCF model achieved recognition results in terms of the Root Mean Square Error (RMSE) and the Mean Absolute Error (MAE) with the values of 7.75/5.96 and 7.49/5.99, respectively, demonstrating its superiority to most existing benchmark methods, verifying the rationality and effectiveness of our approach.
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表 1 不同数量的PLEGDF在AVEC2013数据集上的比较结果
方法 RMSE MAE ResNet50 8.57 6.44 ResNet50+layer1嵌入PLEGDF 8.11 6.19 ResNet50+layer1，2嵌入PLEGDF 7.91 6.07 PLEGDF- FGLSCF 7.75 5.96 
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表 2 不同数量的PLEGDF在AVEC2014数据集上的比较结果
方法 RMSE MAE ResNet50 8.34 6.37 ResNet50+layer1嵌入PLEGDF 8.09 6.16 ResNet50+layer1，2嵌入PLEGDF 7.88 6.10 PLEGDF- FGLSCF 7.49 5.99
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表 3 在AVEC2013数据集上的比较结果
方法 RMSE MAE 拼接全连接层 8.02 6.14 不同分支识别分数求平均 8.25 6.28 仅使用人脸分支 8.32 6.46 仅使用眼睛区域分支 8.29 6.39 PLEGDF-FGLSCF 7.75 5.96
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表 4 在AVEC2014数据集上的比较结果
方法 RMSE MAE 拼接全连接层 8.49 6.27 不同分支识别分数求平均 8.36 6.28 仅使用人脸分支 8.28 6.16 仅使用眼睛区域分支 8.07 6.21 PLEGDF-FGLSCF 7.49 5.99
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表 5 在 AVEC2013 数据集上识别抑郁强度的方案比较
模型是否预训练 方法 RMSE MAE 未预训练 Valstar等人[40] 13.61 10.88 预训练 Zhu等人[18] 9.82 7.58 预训练 Jazaery等人[30] 9.28 7.37 预训练 Melo等人[24] 7.90 5.98 预训练 Melo等人[25] 7.55 6.24 预训练 Pan等人[26] 7.98 6.15 预训练 Zhou等人[28] 8.28 6.20 预训练 Melo等人[29] 8.25 6.30 预训练 孙浩浩等人[34] 8.70 6.74 未预训练 Uddin等人[19] 8.93 7.04 未预训练 He等人[31] 8.39 6.59 未预训练 Shang等人[35] 8.20 6.38 未预训练 PLEGDF-FGLSCF 7.75 5.96
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表 6 在AVEC2014 数据集上识别抑郁强度的方案比较
模型是否预训练 方法 RMSE MAE 未预训练 Valstar等人[41] 10.86 8.86 预训练 Zhu等人[18] 9.55 7.47 预训练 Jazaery等人[30] 9.20 7.22 预训练 Melo等人[24] 7.61 5.82 预训练 Melo等人[25] 7.65 6.06 预训练 Pan等人[26] 7.75 6.00 预训练 Zhou等人[28] 8.39 6.21 预训练 Melo等人[29] 8.23 6.15 预训练 孙浩浩等人[34] 8.56 6.65 未预训练 Uddin等人[19] 8.78 6.86 未预训练 He等人[31] 8.30 6.51 未预训练 Shang等人[35] 7.84 6.08 未预训练 PLEGDF-FGLSCF 7.49 5.99
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表 7 对于无抑郁样本和不同抑郁程度样本模型产生的PCC结果
抑郁程度 AVEC2013 AVEC2014 无抑郁(0～13分) 0.575 0.497 轻度抑郁(14～19分) 0.772 0.689 中度抑郁(20～28分) 0.661 0.782 重度抑郁(29～63分) 0.867 0.855
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表 8 模型在跨数据集上的测试结果
训练集 测试集 RMSE MAE 实验1 AVEC2013 AVEC2014 7.84 5.79 实验2 AVEC2014 AVEC2013 8.57 6.48 实验3 AVEC2013 北风任务 7.78 5.96 实验4 AVEC2013 自由任务 7.91 5.94 实验5 北风任务 AVEC2013 8.76 6.65 实验6 自由任务 AVEC2013 8.32 6.27 实验7 北风任务 自由任务 7.99 6.02 实验8 自由任务 北风任务 8.81 6.77
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