From e3dbf1f96eda4a16e741c0135fc5e7e90a90eeac Mon Sep 17 00:00:00 2001 From: Sefik Ilkin Serengil Date: Fri, 4 Dec 2020 11:21:28 +0300 Subject: [PATCH] elastic face --- README.md | 10 ++++++---- 1 file changed, 6 insertions(+), 4 deletions(-) diff --git a/README.md b/README.md index 3a15ad8..ec8d0b7 100644 --- a/README.md +++ b/README.md @@ -45,10 +45,6 @@ df = DeepFace.find(img_path = "img1.jpg", db_path = "C:/workspace/my_db")

-**Large Scale Face Recognition** - [`Demo with Elasticsearch`](https://youtu.be/i4GvuOmzKzo), [`Demo with Spotify Annoy`](https://youtu.be/Jpxm914o2xk) - -Notice that face recognition has O(n) time complexity and this might be problematic for millions level data. Herein, approximate nearest neighbor (a-nn) algorithm reduces the time complexity dramatically. [Spotify Annoy](https://sefiks.com/2020/09/16/large-scale-face-recognition-with-spotify-annoy/), [Facebook Faiss](https://sefiks.com/2020/09/17/large-scale-face-recognition-with-facebook-faiss/) and [NMSLIB](https://sefiks.com/2020/09/19/large-scale-face-recognition-with-nmslib/) are amazing a-nn libraries. Besides, [Elasticsearch](https://sefiks.com/2020/11/27/large-scale-face-recognition-with-elasticsearch/) wraps an a-nn algorithm and it offers highly scalable feature. You should run deepface within those a-nn frameworks if you have really large scale data sets. - **Face recognition models** - [`Demo`](https://youtu.be/i_MOwvhbLdI) Deepface is a **hybrid** face recognition package. It currently wraps the **state-of-the-art** face recognition models: [`VGG-Face`](https://sefiks.com/2018/08/06/deep-face-recognition-with-keras/) , [`Google FaceNet`](https://sefiks.com/2018/09/03/face-recognition-with-facenet-in-keras/), [`OpenFace`](https://sefiks.com/2019/07/21/face-recognition-with-openface-in-keras/), [`Facebook DeepFace`](https://sefiks.com/2020/02/17/face-recognition-with-facebook-deepface-in-keras/), [`DeepID`](https://sefiks.com/2020/06/16/face-recognition-with-deepid-in-keras/) and [`Dlib`](https://sefiks.com/2020/07/11/face-recognition-with-dlib-in-python/). The default configuration verifies faces with **VGG-Face** model. You can set the base model while verification as illustared below. @@ -115,6 +111,12 @@ user │ │ ├── Bob.jpg ``` +**Large Scale Face Recognition** - [`Demo with Elasticsearch`](https://youtu.be/i4GvuOmzKzo), [`Demo with Spotify Annoy`](https://youtu.be/Jpxm914o2xk) + +Notice that face recognition has O(n) time complexity and this might be problematic for millions level data. Herein, approximate nearest neighbor (a-nn) algorithm reduces the time complexity dramatically. [Spotify Annoy](https://sefiks.com/2020/09/16/large-scale-face-recognition-with-spotify-annoy/), [Facebook Faiss](https://sefiks.com/2020/09/17/large-scale-face-recognition-with-facebook-faiss/) and [NMSLIB](https://sefiks.com/2020/09/19/large-scale-face-recognition-with-nmslib/) are amazing a-nn libraries. Besides, [Elasticsearch](https://sefiks.com/2020/11/27/large-scale-face-recognition-with-elasticsearch/) wraps an a-nn algorithm and it offers highly scalable feature. You should run deepface within those a-nn frameworks if you have really large scale data sets. + +

+ **Face Detectors** - [`Demo`](https://youtu.be/GZ2p2hj2H5k) Face detection and alignment are early stages of a modern face recognition pipeline. [OpenCV haar cascade](https://sefiks.com/2020/02/23/face-alignment-for-face-recognition-in-python-within-opencv/), [SSD](https://sefiks.com/2020/08/25/deep-face-detection-with-opencv-in-python/), [Dlib](https://sefiks.com/2020/07/11/face-recognition-with-dlib-in-python/) and [MTCNN](https://sefiks.com/2020/09/09/deep-face-detection-with-mtcnn-in-python/) methods are wrapped in deepface as a detector. You can optionally pass a custom detector to functions in deepface interface. MTCNN is the default detector if you won't pass any detector.