A computer vision-based framework for objective evaluation of sunken upper eyelid

Sunken upper eyelid correction is clinically and aesthetically important in periorbital reconstruction. However, current evaluation methods for sunken upper eyelid are limited by the expensive and complex medical equipment or subjective judgment. This paper proposes a single image assessment framework based on computer vision (CV) that enables objective evaluation of sunken upper eyelid morphology. The method consists of two stages: The first stage is performed to establish a consistent analysis foundation. Facial landmarks are detected using a 106-point model to crop the periocular region, followed by resolution normalization and eyelid subregion segmentation. In the second stage, three clinically relevant features are extracted: Variance of Gray Value (VGV) for surface depression, Structural Similarity Index (SSIM) for bilateral symmetry, and Degree of Eyelid Wrinkles (DEW) for texture irregularity. These features are integrated into a Support Vector Machine (SVM) model, which outputs the L2 distance to the separating hyperplane ( $D_{\left(f\right)}$ ) to score overall morphological features. Statistical results on the samples showed significant differences in VGV, SSIM, and DEW between normal group and patient group. Based on the proposed method, most patients had a positive postoperative to preoperative difference of $\Delta D$ , indicating measurable improvements in surgical outcomes.