arXiv:2610.36834v1 Announce Type: new Abstract: Audio-Visual Target Speaker Extraction (AVTSE) uses face-related visual cues to extract a target speaker from a speech mixture. In many AVTSE systems, visual features are obtained from convolutional image encoders, and their computation may become a non-negligible part of the overall system when lightweight separation backbones are used. This work proposes a dual-branch visual front-end that reorganizes visual information according to its spatial characteristics. A compact lip region of interest is used to model local articulation cues through convolutional processing, while facial landmark coordinates are used to represent global geometric motion with a lightweight temporal convolutional network. The two representations are integrated into IIANet and SpatialNet with limited fusion overhead. Experiments on LRS3-2mix under a unified reduced training protocol show that the proposed method achieves a performance-computation trade-off close to the lightweight image-based baseline. Compared with the ResNet18-based visual encoder, it reduces the module-level visual encoding computation by more than 90% across both evaluated separation backbones, with a moderate performance gap. This comparison excludes face detection and landmark estimation and therefore applies to systems where landmark coordinates are already available from preprocessing. Demos are available at https://anonymous.4open.science/r/Lightweight-Visual-Frontend-E46E.