To address the limitations of traditional convolutional networks in handling partial correlations and feature redundancy, leading to suboptimal facial expression recognition accuracy, this paper proposes a lightweight micro-expression recognition method. This method enhances image classification performance by integrating Sparse Inverse Covariance Estimation (SICE) and Spatial Channel Reconstruction Convolution (SCConv). Initially, incorporating SICE as a new network layer into ResNet50 enables the model to more effectively identify and utilize partial correlations among features, helping to reduce bias caused by confounding variables. Subsequently, the introduction of SCConv, replacing standard convolutional layers, optimizes the model structure through Spatial Reconstruction Units (SRU) and Channel Reconstruction Units (CRU). Finally, experimental results indicate that the facial expression recognition network based on sparse inverse covariance estimation achieved a classification accuracy of 90.75% on facial expression data, significantly outperforming other network models. The studied model demonstrated a reduction in both parameter count and computational cost, with values of 20.13M and 3.61GFlops, respectively, when compared to other models. This suggests that sparse inverse covariance estimation combined with spatial channel convolution can further enhance classification performance in facial expression image.