Efficient and Interpretable Body-Based Emotion Recognition with Lightweight Temporal Convolutional Networks
Body-based emotion recognition is important for real-time affective systems, but graph-based skeleton models can be computationally expensive. This paper studies whether lightweight temporal convolutional networks (TCNs) can provide an efficient and interpretable alternative for body-based emotion classification. We evaluate a family of TCN models on DIEM-A and compare them with a graph-based time-series graph (G-TSG) baseline using accuracy, macro-F1, parameter count, and inference latency. Although G-TSG achieves the highest mean performance, TCN-Base remains within $1.58$ accuracy points and $1.25$ macro-F1 points while using $79.18\%$ fewer parameters and reducing classifier latency b...
| Authors | Christian Arzate Cruz, Stefanos Gkikas, Houshyar Asadi |
|---|---|
| Primary category | cs.AI |
| Published | 23 July 2026 |
| Last updated at source | 27 July 2026 |
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