From 63d919ff32cbf9c6d1395266862d70a994df3e18 Mon Sep 17 00:00:00 2001 From: Sefik Ilkin Serengil Date: Sat, 30 May 2020 10:25:06 +0300 Subject: [PATCH] links --- README.md | 10 ++++++---- 1 file changed, 6 insertions(+), 4 deletions(-) diff --git a/README.md b/README.md index 8874266..e863e18 100644 --- a/README.md +++ b/README.md @@ -16,7 +16,7 @@ pip install deepface ## Face Recognition -A modern face recognition pipeline consists of 4 common stages: [detect](https://sefiks.com/2020/02/23/face-alignment-for-face-recognition-in-python-within-opencv/), [align](https://sefiks.com/2020/02/23/face-alignment-for-face-recognition-in-python-within-opencv/), [represent](https://sefiks.com/2018/08/06/deep-face-recognition-with-keras/) and [verify](https://sefiks.com/2020/05/22/fine-tuning-the-threshold-in-face-recognition/). **deepface** handles all these common stages in the background. +A modern [face recognition pipeline](https://sefiks.com/2020/05/01/a-gentle-introduction-to-face-recognition-in-deep-learning/) consists of 4 common stages: [detect](https://sefiks.com/2020/02/23/face-alignment-for-face-recognition-in-python-within-opencv/), [align](https://sefiks.com/2020/02/23/face-alignment-for-face-recognition-in-python-within-opencv/), [represent](https://sefiks.com/2018/08/06/deep-face-recognition-with-keras/) and [verify](https://sefiks.com/2020/05/22/fine-tuning-the-threshold-in-face-recognition/). **deepface** handles all these common stages in the background. **Face Verification** - [`Demo`](https://youtu.be/KRCvkNCOphE) @@ -45,7 +45,7 @@ Items of resp_obj might be unsorted when you pass multiple instances to verify f **Large scale face recognition** - [`Demo`](https://youtu.be/Hrjp-EStM_s) -You can apply face recognition on a large scale data set as well. Face recognition requires to apply face verification multiple times. Herein, deepface offers an out-of-the-box find function to handle this action. Representations of faces photos in your database folder will be stored in a pickle file when find function is called once. Then, deepface just finds representation of the target image. In this way, finding an identity in a large scale data set will be performed in just seconds. +You can apply face recognition on a [large scale](https://sefiks.com/2020/05/25/large-scale-face-recognition-for-deep-learning/) data set as well. Face recognition requires to apply face verification multiple times. Herein, deepface offers an out-of-the-box find function to handle this action. Representations of faces photos in your database folder will be stored in a pickle file when find function is called once. Then, deepface just finds representation of the target image. In this way, finding an identity in a large scale data set will be performed in just seconds. ```python from deepface import DeepFace @@ -88,7 +88,9 @@ DeepFace.verify("img1.jpg", "img2.jpg", model_name = "VGG-Face", model = model) **Similarity** -Face recognition models are regular [convolutional neural networks](https://sefiks.com/2018/03/23/convolutional-autoencoder-clustering-images-with-neural-networks/) and they are responsible to represent face photos as vectors. Decision of verification is based on the distance between vectors. We can classify pairs if its distance is less than a [threshold](https://sefiks.com/2020/05/22/fine-tuning-the-threshold-in-face-recognition/). Distance could be found by different metrics such as [Cosine Similarity](https://sefiks.com/2018/08/13/cosine-similarity-in-machine-learning/), Euclidean Distance and L2 form. The default configuration finds the **cosine similarity**. You can alternatively set the similarity metric while verification as demostratred below. +Face recognition models are regular [convolutional neural networks](https://sefiks.com/2018/03/23/convolutional-autoencoder-clustering-images-with-neural-networks/) and they are responsible to represent face photos as vectors. Decision of verification is based on the distance between vectors. We can classify pairs if its distance is less than a [threshold](https://sefiks.com/2020/05/22/fine-tuning-the-threshold-in-face-recognition/). + +Distance could be found by different metrics such as [Cosine Similarity](https://sefiks.com/2018/08/13/cosine-similarity-in-machine-learning/), Euclidean Distance and L2 form. The default configuration finds the **cosine similarity**. You can alternatively set the similarity metric while verification as demostratred below. ```python result = DeepFace.verify("img1.jpg", "img2.jpg", model_name = "VGG-Face", distance_metric = "cosine") @@ -169,7 +171,7 @@ BTW, you should use regular slash ( / ) instead of backslash ( \ ) in Windows OS Deepface serves an API as well. -You can clone [`/api/api.py`](https://github.com/serengil/deepface/tree/master/api/api.py) and pass it to python command as an argument. This will get a rest service up. In this way, you can call deepface from an external system. +You can clone [`/api/api.py`](https://github.com/serengil/deepface/tree/master/api/api.py) and pass it to python command as an argument. This will get a rest service up. In this way, you can call deepface from an external system such as mobile app or web.