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Qingxiang Wang

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AAAI Conference 2026 Conference Paper

Dep-MAP: A Multi-level Alignment Framework with Semantic Prototypes for Video-based Automatic Depression Assessment

Hao Wang
Jiayu Ye
Qingxiang Wang

Spatiotemporal analysis of facial behavior is a crucial method for evaluating the mental state of depression patients. However, in practice, depressed patients often display facial behaviors similar to healthy individuals due to masking tendencies. Additionally, facial expressions among depressed patients are also different, increasing the difficulty of assessment. To address this, we propose a video-based automatic depression assessment model Dep-MAP for complex facial behaviors of depression patients. Dep-MAP adopts a dual-branch architecture to extract visual features of facial behavior and capture corresponding emotional semantic features. Specifically, the extracted deep semantic features are clustered, resulting in semantically distinct prototype sets, where each severity group learns a set of discriminative facial behavior prototype representations, to suppress inter-class semantic confusion. Subsequently, we propose a semantic prototype-supervised contrastive learning method, which aligns latent semantics between shallow and deep features, realizing emotional semantic guidance and self-knowledge distillation for the visual feature branch, effectively suppressing intra-class difference. Then, we integrate key depression cues across multiple spatiotemporal scales via a multi-scale weighted fusion strategy, achieving automatic depression assessment. Experimental results demonstrate that Dep-MAP effectively identifies potential key frames in temporal sequences, and aggregates key frame representations with semantic consistency, achieving significantly superior state-of-the-art results on the AVEC2013 and AVEC2014 public datasets.

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JBHI Journal 2026 Journal Article

MFE-Former: Disentangling Emotion-Identity Dynamics via Self-Supervised Learning for Enhancing Speech-Driven Depression Detection

Hao Wang
Jiayu Ye
Yanhong Yu
Lin Lu
Lin Yuan
Qingxiang Wang

Acoustic features are crucial behavioral indicators for depression detection. However, prior speech-based depression detection methods often overlook the variability of emotional patterns across samples, leading to interference from speaker identity and hindering the effective extraction of emotional changes. To address this limitation, we developed the Emotional Word Reading Experiment (EWRE) and introduced a method combining self-supervised and supervised learning for depression detection from speech called MFE-Former. First, we generate fine-grained emotional representations for response segments by computing cosine similarity between intra-sample and inter-sample contexts. Concurrently, orthogonality constraints decouple identity information from emotional features, while a Transformer decoder reconstructs spectral structures to improve sensitivity to depression-related emotional patterns. Next, we propose a multi-scale emotion change perception module and a Bernoulli distribution-based joint decision module integrate multi-level information for depression detection. By enhancing the distribution differences among positive, neutral, and negative emotional features, we find that patients with depression are more inclined to express negative emotions, whereas healthy individuals express more positive emotions. The experimental results on EWRE and AVEC 2014 show that MFE-Former outperforms state-of-the-art temporal methods under conditions of variability in emotional patterns across samples.

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EAAI Journal 2025 Journal Article

Depression and anxiety detection method based on serialized facial expression imitation

Lin Lu
Yan Jiang
Xingyun Li
Hao Wang
Qingzhi Zou
Qingxiang Wang

Facial recognition techniques are widely employed for automatic detection of depression and anxiety. However, current studies overlook the impact of varying spatial resolutions on model performance and lack a mechanism to share attention regions across sequential data. To advance research in this area, we conducted the Voluntary Facial Expression Mimicry Experiment (VFEM) and constructed the VFEM dataset. We also introduce the SFE-Former, a sequential facial expression recognition model designed for detecting depression and anxiety. SFE-Former features a mechanism that shares attention regions across sequence data, allowing each data point to enhance its features by leveraging shared information. Additionally, the model integrates features from different scales using fusion and weighting strategies. The experimental results indicate that SFE-Former achieved impressive accuracy rate: 0. 893 for depression detection, 0. 889 for anxiety detection, and 0. 780 for co-occurrence detection of depression and anxiety. Meanwhile, SFE-Former also obtained state-of-the-art (SOAT) results on AVEC2014 dataset. This work can enhance the accuracy of identifying patients with depression and anxiety, providing doctors with reliable auxiliary diagnosis. The source code for SFE-Former is accessible at https: //github. com/lulin-6k/SFE-Former.

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JBHI Journal 2023 Journal Article

Analysis and Recognition of Voluntary Facial Expression Mimicry Based on Depressed Patients

Jiayu Ye
Yanhong Yu
Gang Fu
Yunshao Zheng
Yang Liu
Yitao Zhu
Qingxiang Wang

Many clinical studies have shown that facial expression recognition and cognitive function are impaired in depressed patients. Different from spontaneous facial expression mimicry (SFEM), 164 subjects (82 in a case group and 82 in a control group) participated in our voluntary facial expression mimicry (VFEM) experiment using expressions of neutrality, anger, disgust, fear, happiness, sadness and surprise. Our research is as follows. First, we collected a large amount of subject data for VFEM. Second, we extracted the geometric features of subject facial expression images for VFEM and used Spearman correlation analysis, a random forest, and logistic regression-based recursive feature elimination (LR-RFE) to perform feature selection. The features selected revealed the difference between the case group and the control group. Third, we combined geometric features with the original images and improved the advanced deep learning facial expression recognition (FER) algorithms in different systems. We propose the E-ViT and E-ResNet based on VFEM. The accuracies and F1 scores were higher than those of the baseline models, respectively. Our research proved that it is effective to use feature selection to screen geometric features and combine them with a deep learning model for depression facial expression recognition.

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