# Patch-based Probabilistic Image Quality Assessment for Face Selection and Improved Video-based Face Recognition

arXiv: https://arxiv.org/abs/1304.0869 · HTML: https://mlsanity.com/paper/1304.0869/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Apr 2013.

## Built on by (most cited first, 13 of 13)

- [SDD-FIQA: Unsupervised Face Image Quality Assessment with Similarity Distribution Distance](https://mlsanity.com/paper/2103.05977/) (2021)
- [About Face: A Survey of Facial Recognition Evaluation](https://mlsanity.com/paper/2102.00813/) (2021)
- [QMagFace: Simple and Accurate Quality-Aware Face Recognition](https://mlsanity.com/paper/2111.13475/) (2021)
- [Pixel-Level Face Image Quality Assessment for Explainable Face Recognition](https://mlsanity.com/paper/2110.11001/) (2021)
- [Real-World Super-Resolution of Face-Images from Surveillance Cameras](https://mlsanity.com/paper/2102.03113/) (2021)
- [A Study on the Impact of Face Image Quality on Face Recognition in the Wild](https://mlsanity.com/paper/2307.02679/) (2023)
- [On Responsible Machine Learning Datasets with Fairness, Privacy, and Regulatory Norms](https://mlsanity.com/paper/2310.15848/) (2023)
- [Densely Deformable Efficient Salient Object Detection Network](https://mlsanity.com/paper/2102.06407/) (2021)
- [Rank-based verification for long-term face tracking in crowded scenes](https://mlsanity.com/paper/2107.13273/) (2021)
- [Intelligent Frame Selection as a Privacy-Friendlier Alternative to Face Recognition](https://mlsanity.com/paper/2101.07529/) (2021)
- [Deep Tiny Network for Recognition-Oriented Face Image Quality Assessment](https://mlsanity.com/paper/2106.04852/) (2021)
- [Peng Cheng Object Detection Benchmark for Smart City](https://mlsanity.com/paper/2203.05949/) (2022)
- [FaceQSORT: a Multi-Face Tracking Method based on Biometric and Appearance Features](https://mlsanity.com/paper/2501.11741/) (2025)

## Abstract (arXiv)

In video based face recognition, face images are typically captured over multiple frames in uncontrolled conditions, where head pose, illumination, shadowing, motion blur and focus change over the sequence. Additionally, inaccuracies in face localisation can also introduce scale and alignment variations. Using all face images, including images of poor quality, can actually degrade face recognition performance. While one solution it to use only the "best" subset of images, current face selection techniques are incapable of simultaneously handling all of the abovementioned issues. We propose an efficient patch-based face image quality assessment algorithm which quantifies the similarity of a face image to a probabilistic face model, representing an "ideal" face. Image characteristics that affect recognition are taken into account, including variations in geometric alignment (shift, rotation and scale), sharpness, head pose and cast shadows. Experiments on FERET and PIE datasets show that the proposed algorithm is able to identify images which are simultaneously the most frontal, aligned, sharp and well illuminated. Further experiments on a new video surveillance dataset (termed ChokePoint) show that the proposed method provides better face subsets than existing face selection techniques, leading to significant improvements in recognition accuracy.