  Portrait images typically consist of a salient person against diverse
backgrounds. With the development of mobile devices and image processing
techniques, users can conveniently capture portrait images anytime and
anywhere. However, the quality of these portraits may suffer from the
degradation caused by unfavorable environmental conditions, subpar photography
techniques, and inferior capturing devices. In this paper, we introduce a
dual-branch network for portrait image quality assessment (PIQA), which can
effectively address how the salient person and the background of a portrait
image influence its visual quality. Specifically, we utilize two backbone
networks (\textit{i.e.,} Swin Transformer-B) to extract the quality-aware
features from the entire portrait image and the facial image cropped from it.
To enhance the quality-aware feature representation of the backbones, we
pre-train them on the large-scale video quality assessment dataset LSVQ and the
large-scale facial image quality assessment dataset GFIQA. Additionally, we
leverage LIQE, an image scene classification and quality assessment model, to
capture the quality-aware and scene-specific features as the auxiliary
features. Finally, we concatenate these features and regress them into quality
scores via a multi-perception layer (MLP). We employ the fidelity loss to train
the model via a learning-to-rank manner to mitigate inconsistencies in quality
scores in the portrait image quality assessment dataset PIQ. Experimental
results demonstrate that the proposed model achieves superior performance in
the PIQ dataset, validating its effectiveness. The code is available at
\url{https://github.com/sunwei925/DN-PIQA.git}.
