# MF-GCN: A Multi-Frequency Graph Convolutional Network for Tri-Modal Depression Detection Using Eye-Tracking, Facial, and Acoustic Features

arXiv: https://arxiv.org/abs/2511.15675 · HTML: https://mlsanity.com/paper/2511.15675/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 22 of its 129 references that are in the map.
- First posted on arXiv 19 Nov 2025.

## Builds on (most cited first, 22 of 22)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [WaveNet: A Generative Model for Raw Audio](https://mlsanity.com/paper/1609.03499/) (2016)
- [CNN Architectures for Large-Scale Audio Classification](https://mlsanity.com/paper/1609.09430/) (2016)
- [What do different evaluation metrics tell us about saliency models?](https://mlsanity.com/paper/1604.03605/) (2016)
- [pyannote.audio: neural building blocks for speaker diarization](https://mlsanity.com/paper/1911.01255/) (2019)
- [Deep Learning for Audio Signal Processing](https://mlsanity.com/paper/1905.00078/) (2019)
- [Very Deep Convolutional Neural Networks for Raw Waveforms](https://mlsanity.com/paper/1610.00087/) (2016)
- [Estimating Continuous Distributions in Bayesian Classifiers](https://mlsanity.com/paper/1302.4964/) (2013)
- [Learning from Between-class Examples for Deep Sound Recognition](https://mlsanity.com/paper/1711.10282/) (2017)
- [Cardiologist-Level Arrhythmia Detection with Convolutional Neural Networks](https://mlsanity.com/paper/1707.01836/) (2017)
- [MODMA dataset: a Multi-modal Open Dataset for Mental-disorder Analysis](https://mlsanity.com/paper/2002.09283/) (2020)
- [Bridging the Gap Between Spectral and Spatial Domains in Graph Neural Networks](https://mlsanity.com/paper/2003.11702/) (2020)
- [Instagram photos reveal predictive markers of depression](https://mlsanity.com/paper/1608.03282/) (2016)
- [Deep Learning for Depression Recognition with Audiovisual Cues: A Review](https://mlsanity.com/paper/2106.00610/) (2021)
- [MFCC-based Recurrent Neural Network for Automatic Clinical Depression Recognition and Assessment from Speech](https://mlsanity.com/paper/1909.07208/) (2019)
- [Is Cross-Attention Preferable to Self-Attention for Multi-Modal Emotion Recognition?](https://mlsanity.com/paper/2202.09263/) (2022)
- [Progressive Neural Networks for Transfer Learning in Emotion Recognition](https://mlsanity.com/paper/1706.03256/) (2017)
- [Feature Learning for Chord Recognition: The Deep Chroma Extractor](https://mlsanity.com/paper/1612.05065/) (2016)
- [FacePsy: An Open-Source Affective Mobile Sensing System -- Analyzing Facial Behavior and Head Gesture for Depression Detection in Naturalistic Settings](https://mlsanity.com/paper/2406.17181/) (2024)
- [Classifying Eye-Tracking Data Using Saliency Maps](https://mlsanity.com/paper/2010.12913/) (2020)

## Abstract (arXiv)

Depression is a prevalent global mental health disorder, characterised by persistent low mood and anhedonia. However, it remains underdiagnosed because current diagnostic methods depend heavily on subjective clinical assessments. To enable objective detection, we introduce a gold standard dataset of 103 clinically assessed participants collected through a tripartite data approach which uniquely integrated eye tracking data with audio and video to give a comprehensive representation of depressive symptoms. Eye tracking data quantifies the attentional bias towards negative stimuli that is frequently observed in depressed groups. Audio and video data capture the affective flattening and psychomotor retardation characteristic of depression. Statistical validation confirmed their significant discriminative power in distinguishing depressed from non depressed groups. We address a critical limitation of existing graph-based models that focus on low-frequency information and propose a Multi-Frequency Graph Convolutional Network (MF-GCN). This framework consists of a novel Multi-Frequency Filter Bank Module (MFFBM), which can leverage both low and high frequency signals. Extensive evaluation against traditional machine learning algorithms and deep learning frameworks demonstrates that MF-GCN consistently outperforms baselines. In binary classification, the model achieved a sensitivity of 0.96 and F2 score of 0.94. For the 3 class classification task, the proposed method achieved a sensitivity of 0.79 and specificity of 0.87 and siginificantly suprassed other models. To validate generalizability, the model was also evaluated on the Chinese Multimodal Depression Corpus (CMDC) dataset and achieved a sensitivity of 0.95 and F2 score of 0.96. These results confirm that our trimodal, multi frequency framework effectively captures cross modal interaction for accurate depression detection.