Emotion recognition from macroexpression and microexpression has been widely used in applications such as human–computer interaction, learning status evaluation, and mental disorder diagnosis. However, due to the complexity of human macroexpressions, recognizing macroexpressions with high accuracy is a challenging task. Moreover, the short duration and low movement intensity of microexpressions make its recognition more difficult. For MM-FER (macro and microfacial expression recognition), the key information can be more efficiently expressed by a graph. In this article, a novel framework based on graph neural network named SSGNN (spatial and spectral domain features based on a graph neural network) is designed to extract spatial and spectral domain features from facial images for MM-FER, which can efficiently recognize both macroexpressions and microexpressions under the same model. SSGNN consists of two parts, SPAGNN and SPEGNN, which are used to extract spectral and spatial domain features, respectively. Experiments proved that jointly using the spectral and spatial information extracted by SSGNN can largely improve the performance of MM-FER when the training sample is limited. First, the influences of different neighbors and samples to the model performance was analyzed. Then, the contribution of SPAGNN and SPEGNN were evaluated. It was discovered that fusing the result of SPAGNN and SPEGNN at decision level further improved the performance of MM-FER. Experiment proved that SSGNN can recognize microexpression acquired by various sensors with higher accuracy under different image resolutions and image formats than the compared state-of-the-art methods in most cases. A cross-dataset experiment demonstrated the generalization ability of SSGNN.