A Multi-Model Deep Learning Framework Combining FLAME-MAE, Temporal-Shift Encoding, and Transformer Architectures for MicroExpression-Based Mental Health Monitoring: A Literature Review and Proposed Architecture

Abstract

Micro-expressions are involuntary, sub-second facial movements that reveal concealed affective states, making them a promising signal for objective, low-cost mental-health screening. Existing facial-analysis research spans face privacy and deidentification, action-unit and expression recognition, deepfake and face-editing methods, robustness and domain adaptation, and micro-expression and psychological modeling — yet, as this review documents through a tabulated survey of twenty-five recent studies, no single existing technique jointly provides geometry-aware structural modeling, millisecond-scale temporal sensitivity, global contextual reasoning, clinical interpretability, and identity-invariance in one framework. This paper reviews that literature, tabulates the specific technique, reported outcome, and limitation of each cited study, and consolidates the resulting research gap. It then presents a proposed multi-model framework — FTT-Net, DB-FTME, and FGT-MT — built on FLAME-inspired masked-autoencoder landmark modeling (FLAME-MAE), a temporal-shift micro-expression encoder (TSMEN-style), and Transformer-based global reasoning, organized directly around four design objectives: high-accuracy recognition, real-time deployability, clinical interpretability, and unified mental-health inference. A block diagram, an IEEEstyle algorithm listing, and a problem analysis drawn from prior conference-stage and journal-stage evaluations of a related geometry–temporal–Transformer system are used to substantiate the objectives that motivate the proposed design, and target performance outcomes are stated as design objectives to be validated experimentally rather than as already-achieved results.