---
# System prepended metadata

title: face image quality assessment
tags: [生物辨識]

---

---
tags: 生物辨識
---

# face image quality assessment


## Motvation
* 人工標記人臉資料的品質是很麻煩又非常容易出錯的
* 不是要分辨出人看起來清晰的圖像，還是要找出模型可以正確判斷的


## Method

#### SER-FIQ: Unsupervised Estimation of Face Image Quality Based on Stochastic Embedding Robustness
[github](https://github.com/pterhoer/FaceImageQuality)

透過dropout層產出隨機不同embedding，兩兩計算產生質量分數

![](https://i.imgur.com/W0Uxlji.png)

分數計算公式如下
![](https://i.imgur.com/ca5eDFb.png)

![](https://i.imgur.com/CxpjQQA.png)


#### SDD-FIQA: Unsupervised Face Image Quality Assessment with Similarity Distribution Distance
[github](https://github.com/Tencent/TFace/tree/quality)


對每一張圖片隨機選m個類內及m個類外，利用Wasserstein Distance算出分數
![](https://i.imgur.com/zZ6fzAK.png)

[Wasserstein Distance](https://zhuanlan.zhihu.com/p/353418080)

Quality Model:將Recognition model拿掉embedding layer和classification layer加上一個fully-connected layer並利用Huber loss訓練
![](https://i.imgur.com/eK5TBlE.png)
![](https://i.imgur.com/uFjYmMv.png)


