Two-Stage Temporal Modelling Framework for Video-Based Depression Recognition Using Graph Representation

Abstract

Video-based automatic depression analysis provides a fast, objective and repeatable self-assessment solution, which has been widely developed in recent years. While depression cues may be reflected by human facial behaviours of various temporal scales, most existing approaches either focused on modelling depression from short-term or video-level facial behaviours. In this sense, we propose a two-stage framework that models depression severity from multi-scale short-term and video-level facial behaviours. The short-term depressive behaviour modelling stage first deep learns depression-related facial behavioural features from multiple short temporal scales, where a Depression Feature Enhancement (DFE) module is proposed to enhance the depression-related cues for all temporal scales and remove non-depression related noise. Two novel graph encoding strategies are proposed in the video-level depressive behavior modeling stage, i.e., Sequential Graph Representation (SEG) and Spectral Graph Representation (SPG), to re-encode all short-term features of the target video into a video-level graph representation, summarizing depression-related multi-scale video-level temporal information. As a result, the produced graph representations predict depression severity using both short-term and long-term facial behaviour patterns. The experimental results on AVEC 2013, AVEC 2014 and AVEC 2019 datasets show that the proposed DFE module constantly enhanced the depression severity estimation performance for various CNN models while the SPG is superior than other video-level modelling methods. More importantly, the result achieved for the proposed two-stage framework shows its promising and solid performance compared to widely-used one-stage modelling approaches.

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