Facial emotion recognition (FER) can provide supplementary affective information for human–robot interaction, but deployment on resource-constrained assistive robots requires a balance between recognition performance and computational efficiency. This study presents an edge-based FER framework using a compact CNN operating on 48 × 48 grayscale facial images and retaining all seven FER-2013 expression categories. Square-root-smoothed inverse-frequency weighting is employed to mitigate class imbalance without excessively emphasizing rare classes. On the held-out FER-2013 test set, the proposed model achieves 63.78% Accuracy and 59.32% Macro-F1, achieving higher Accuracy and Macro-F1 than the evaluated ImageNet-pretrained MobileNetV2 and MobileNetV3-Small baselines. INT8 post-training quantization reduces model size by 74.13% relative to FP32, with decreases of only 0.91 and 0.50 percentage points in Accuracy and Macro-F1, respectively. On a Raspberry Pi 3 Model B+, INT8 achieves a mean model-only inference latency of 19.30 ms and a model-only throughput of 51.82 FPS. Using an actor-disjoint RAVDESS protocol comprising 416 videos, EMA stabilization reduces prediction switching by 54.86% on the held-out test actors. These results support the feasibility of compact edge-based FER for assistive robotic interaction while emphasizing that the framework provides supplementary affective cues rather than clinical diagnosis or autonomous decision-making.