A novel framework for aesthetic assessment of portrait sketches via multi-feature integration and self-supervised learning

Abstract

Image Aesthetic Assessment (IAA) has developed rapidly in recent years, but the automated assessment of sketch portraits, as a core part of formal art examinations, remains largely unexplored. To fill this gap, we construct the Sketch Head Portrait Dataset (SHPD), the first large-scale, publicly available dataset containing 14,084 sketch portraits, of which 1,339 are rated by experts. Based on SHPD, we propose the Sketch Paintings Aesthetic Assessment Network (SPAAN), which aims to provide accurate and efficient aesthetic assessment. SPAAN integrates three complementary feature streams: a general feature network captures global compositional cues, while two self-supervised sketch feature networks learn contour lines and value scale features through aesthetic quality degradation pretext task. These feature streams are re-weighted and aggregated through a lightweight multi-feature optimization and fusion module through a channel attention mechanism and a multi-layer perceptron-based weighting algorithm to simulate the multi-dimensional scoring criteria in real sketch evaluation scenarios. Extensive experiments on SHPD show that SPAAN outperforms mainstream general aesthetic assessment methods, verifying the effectiveness of our adopted self-supervised learning method and fusion strategy. This work contributes to advancing large-scale portrait sketch assessment tasks and provides a new research direction for artistic image aesthetic assessment (AIAA). Dataset and code are available at: https://gitee.com/yongzhenke/SPAAN.