Semantic Style Transfer for Paintings Using Convolutional Neural Networks (CNNs)

In recent years, the importance of photographic portrait styles has garnered significant attention, prompting numerous researchers to explore innovative methods for modifying and enhancing these styles. Neural style transfer has advanced rapidly for photographic portraits, yet transferring painterly styles to human facial and body images remains difficult because global stylization frequently distorts facial geometry and erases identity. This study presents a semantic, region-wise painting style transfer framework based on Convolutional Neural Networks (CNNs) that preserves facial identity and semantic structure during stylization. The method parses both the source photograph and an example painting into corresponding semantic regions, comprising ten facial components together with hair, chest, arms, legs, and background, and transfers the style region by region so that each part is stylized from its semantic counterpart. A feature reconstruction stage based on Gram matrix style representations minimizes content and style loss within each region, while a part-based generation and fusion stage augmented with Laplacian pyramid decomposition improves local to global consistency and identity preservation. We evaluate the approach with perceptual and identity metrics, reporting Frechet Inception Distance (FID), Structural Similarity (SSIM), and identity cosine similarity (CSIM), and additionally report a downstream classification check as an auxiliary indicator of content preservation. The full model attains an FID of 14.72, an SSIM of 0.82, and a CSIM of 0.86, outperforming GAN and part generation network baselines in identity preservation and realism. We discuss the strengths, limitations, and practical implications of the framework and outline directions toward full-body, high-resolution, and video stylization.

@article{Nazir2026,
  title     = {Semantic Style Transfer for Paintings Using Convolutional Neural Networks (CNNs)},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {17},
  number    = {6},
  year      = {2026},
  publisher = {The Science and Information Organization},
  author    = {Hafiz Muhammad Jamsheed Nazir and Zheng Jiangbin and Omar Alsaleh},
  doi       = {10.14569/IJACSA.2026.0170660},
  url       = {https://doi.org/10.14569/IJACSA.2026.0170660}
}