# Dual-Branch Network for Portrait Image Quality Assessment

arXiv: https://arxiv.org/abs/2405.08555 · HTML: https://mlsanity.com/paper/2405.08555/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 39 of its 78 references that are in the map.
- First posted on arXiv 14 May 2024.

## Builds on (most cited first, 30 of 39)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [MUSIQ: Multi-scale Image Quality Transformer](https://mlsanity.com/paper/2108.05997/) (2021)
- [VGGFace2: A dataset for recognising faces across pose and age](https://mlsanity.com/paper/1710.08092/) (2017)
- [Learning Face Representation from Scratch](https://mlsanity.com/paper/1411.7923/) (2014)
- [KonIQ-10k: An ecologically valid database for deep learning of blind image quality assessment](https://mlsanity.com/paper/1910.06180/) (2019)
- [Blind Image Quality Assessment Using A Deep Bilinear Convolutional Neural Network](https://mlsanity.com/paper/1907.02665/) (2019)
- [Blind Image Quality Assessment via Vision-Language Correspondence: A Multitask Learning Perspective](https://mlsanity.com/paper/2303.14968/) (2023)
- [Deep Neural Networks for No-Reference and Full-Reference Image Quality Assessment](https://mlsanity.com/paper/1612.01697/) (2016)
- [Patch-VQ: 'Patching Up' the Video Quality Problem](https://mlsanity.com/paper/2011.13544/) (2020)
- [A Deep Learning based No-reference Quality Assessment Model for UGC Videos](https://mlsanity.com/paper/2204.14047/) (2022)
- [Blind Quality Assessment for in-the-Wild Images via Hierarchical Feature Fusion and Iterative Mixed Database Training](https://mlsanity.com/paper/2105.14550/) (2021)
- [CR-FIQA: Face Image Quality Assessment by Learning Sample Relative Classifiability](https://mlsanity.com/paper/2112.06592/) (2021)
- [SDD-FIQA: Unsupervised Face Image Quality Assessment with Similarity Distribution Distance](https://mlsanity.com/paper/2103.05977/) (2021)
- [FaceQnet: Quality Assessment for Face Recognition based on Deep Learning](https://mlsanity.com/paper/1904.01740/) (2019)
- [Mobile Computational Photography: A Tour](https://mlsanity.com/paper/2102.09000/) (2021)
- [No-Reference Quality Assessment for 3D Colored Point Cloud and Mesh Models](https://mlsanity.com/paper/2107.02041/) (2021)
- [MD-VQA: Multi-Dimensional Quality Assessment for UGC Live Videos](https://mlsanity.com/paper/2303.14933/) (2023)
- [YOLO5Face: Why Reinventing a Face Detector](https://mlsanity.com/paper/2105.12931/) (2021)
- [MM-PCQA: Multi-Modal Learning for No-reference Point Cloud Quality Assessment](https://mlsanity.com/paper/2209.00244/) (2022)
- [Analysis of Video Quality Datasets via Design of Minimalistic Video Quality Models](https://mlsanity.com/paper/2307.13981/) (2023)
- [Deep Learning based Full-reference and No-reference Quality Assessment Models for Compressed UGC Videos](https://mlsanity.com/paper/2106.01111/) (2021)
- [Advancing Zero-Shot Digital Human Quality Assessment through Text-Prompted Evaluation](https://mlsanity.com/paper/2307.02808/) (2023)
- [IFQA: Interpretable Face Quality Assessment](https://mlsanity.com/paper/2211.07077/) (2022)
- [Going the Extra Mile in Face Image Quality Assessment: A Novel Database and Model](https://mlsanity.com/paper/2207.04904/) (2022)
- [Evaluating Point Cloud from Moving Camera Videos: A No-Reference Metric](https://mlsanity.com/paper/2208.14085/) (2022)
- [Deep Neural Network for Blind Visual Quality Assessment of 4K Content](https://mlsanity.com/paper/2206.04363/) (2022)
- [An Image Quality Assessment Dataset for Portraits](https://mlsanity.com/paper/2304.05772/) (2023)
- [Perceptual Quality Assessment for Digital Human Heads](https://mlsanity.com/paper/2209.09489/) (2022)
- [DDH-QA: A Dynamic Digital Humans Quality Assessment Database](https://mlsanity.com/paper/2212.12734/) (2022)

## Built on by (most cited first, 8 of 8)

- [AGHI-QA: A Subjective-Aligned Dataset and Metric for AI-Generated Human Images](https://mlsanity.com/paper/2504.21308/) (2025)
- [Assessing UHD Image Quality from Aesthetics, Distortions, and Saliency](https://mlsanity.com/paper/2409.00749/) (2024)
- [3DGCQA: A Quality Assessment Database for 3D AI-Generated Contents](https://mlsanity.com/paper/2409.07236/) (2024)
- [Efficient Face Image Quality Assessment via Self-training and Knowledge Distillation](https://mlsanity.com/paper/2507.15709/) (2025)
- [OmniQuality-R: Advancing Reward Models Through All-Encompassing Quality Assessment](https://mlsanity.com/paper/2510.10609/) (2025)
- [Surveillance Facial Image Quality Assessment: A Multi-dimensional Dataset and Lightweight Model](https://mlsanity.com/paper/2602.07403/) (2026)
- [Facial Attractiveness Prediction in Live Streaming: A New Benchmark and Multi-modal Method](https://mlsanity.com/paper/2501.02509/) (2025)
- [LEIQ-Assessor: Multi-dimensional Quality Assessment of Low-light Enhanced Images via Multi-task Learning](https://mlsanity.com/paper/2606.29752/) (2026)

## Abstract (arXiv)

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 (textiti.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 urlhttps://github.com/sunwei925/DN-PIQA.git.