From efc09d3423331fa96eeb12c95102d378c7d73daa Mon Sep 17 00:00:00 2001 From: Sefik Ilkin Serengil Date: Thu, 23 Dec 2021 21:21:18 +0300 Subject: [PATCH] retinaface --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index f45c01f..2b296dd 100644 --- a/README.md +++ b/README.md @@ -162,7 +162,7 @@ Face recognition models are actually CNN models and they expect standard sized i [RetinaFace](https://sefiks.com/2021/04/27/deep-face-detection-with-retinaface-in-python/) and [MTCNN](https://sefiks.com/2020/09/09/deep-face-detection-with-mtcnn-in-python/) seem to overperform in detection and alignment stages but they are much slower. If the speed of your pipeline is more important, then you should use opencv or ssd. On the other hand, if you consider the accuracy, then you should use retinaface or mtcnn. -The performance of RetinaFace is very satisfactory even in the crowd as seen in the following illustration. Besides, it comes with an incredible facial landmark detection performance. Please notice that the landmarks in the detected face areas highlighted with red points. This will improve the alignment score as well. +The performance of RetinaFace is very satisfactory even in the crowd as seen in the following illustration. Besides, it comes with an incredible facial landmark detection performance. Highlighted red points show some facial landmarks such as eyes, nose and mouth. That's why, alignment score of RetinaFace is high as well.