# An Efficient Method for Face Quality Assessment on the Edge

Source: [An Efficient Method for Face Quality Assessment on the Edge](<https://www.sota2.com/research/paper/68fa6d191e209131cea37b22>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2026-01-01T14:54:14.984Z

Authors: Sefa Burak Okcu, Burak O\\u{g}uz \\"Ozkalayc{\\i}, Cevahir \\c{C}{\\i}\\u{g}la

Published: 2022-07-19T18:29:43.000Z

## Abstract

Face recognition applications in practice are composed of two main steps: face detection and feature extraction. In a sole vision-based solution, the first step generates multiple detection for a single identity by ingesting a camera stream. A practical approach on edge devices should prioritize these detection of identities according to their conformity to recognition. In this perspective, we propose a face quality score regression by just appending a single layer to a face landmark detection network. With almost no additional cost, face quality scores are obtained by training this single layer to regress recognition scores with surveillance like augmentations. We implemented the proposed approach on edge GPUs with all face detection pipeline steps, including detection, tracking, and alignment. Comprehensive experiments show the proposed approach's efficiency through comparison with SOTA face quality regression models on different data sets and real-life scenarios.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Face Quality Assessment on Lab surveillance video dataset](<https://www.sota2.com/research/sota/face-quality-assessment-on-lab-surveillance-video-dataset.md>): Face Quality Assessment; Lab surveillance video dataset; BRO: 0.76.

- [Face Quality Assessment on Indoor\_low surveillance video dataset](<https://www.sota2.com/research/sota/face-quality-assessment-on-indoor_low-surveillance-video-dataset.md>): Face Quality Assessment; Indoor\_low surveillance video dataset; BRO: 0.79.

- [Face Quality Assessment on Indoor\_high surveillance video dataset](<https://www.sota2.com/research/sota/face-quality-assessment-on-indoor_high-surveillance-video-dataset.md>): Face Quality Assessment; Indoor\_high surveillance video dataset; Blur Score: not recorded.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2207.09505>)

[PDF](<https://arxiv.org/pdf/2207.09505.pdf>)