deepface实现人脸识别

# deepface <div align="center"> [](https://pepy.tech/project/deepface) [](https://anaconda.org/conda-forge/deepface) [](https://github.com/serengil/deepface/stargazers) [](https://github.com/serengil/deepface/blob/master/LICENSE) [](https://www.patreon.com/serengil?repo=deepface) [](https://twitter.com/serengil) [](https://doi.org/10.1109/ASYU50717.2020.9259802) [](https://doi.org/10.1109/ICEET53442.2021.9659697) </div> <p align="center"><img src="https://raw.githubusercontent.com/serengil/deepface/master/icon/deepface-icon-labeled.png" width="200" height="240"></p> Deepface is a lightweight [face recognition](https://sefiks.com/2018/08/06/deep-face-recognition-with-keras/) and facial attribute analysis ([age](https://sefiks.com/2019/02/13/apparent-age-and-gender-prediction-in-keras/), [gender](https://sefiks.com/2019/02/13/apparent-age-and-gender-prediction-in-keras/), [emotion](https://sefiks.com/2018/01/01/facial-expression-recognition-with-keras/) and [race](https://sefiks.com/2019/11/11/race-and-ethnicity-prediction-in-keras/)) framework for python. It is a hybrid face recognition framework wrapping **state-of-the-art** 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/), [`ArcFace`](https://sefiks.com/2020/12/14/deep-face-recognition-with-arcface-in-keras-and-python/), [`Dlib`](https://sefiks.com/2020/07/11/face-recognition-with-dlib-in-python/) and `SFace`. Experiments show that human beings have 97.53% accuracy on facial recognition tasks whereas those models already reached and passed that accuracy level. ## Installation [](https://pypi.org/project/deepface/) [](https://anaconda.org/conda-forge/deepface) The easiest way to install deepface is to download it from [`PyPI`](https://pypi.org/project/deepface/). It's going to install the library itself and its prerequisites as well. ```shell $ pip install deepface ``` DeepFace is also available at [`Conda`](https://anaconda.org/conda-forge/deepface). You can alternatively install the package via conda. ```shell $ conda install -c conda-forge deepface ``` Then you will be able to import the library and use its functionalities. ```python from deepface import DeepFace ``` **Facial Recognition** - [`Demo`](https://youtu.be/WnUVYQP4h44) A modern [**face recognition pipeline**](https://sefiks.com/2020/05/01/a-gentle-introduction-to-face-recognition-in-deep-learning/) consists of 5 common stages: [detect](https://sefiks.com/2020/08/25/deep-face-detection-with-opencv-in-python/), [align](https://sefiks.com/2020/02/23/face-alignment-for-face-recognition-in-python-within-opencv/), [normalize](https://sefiks.com/2020/11/20/facial-landmarks-for-face-recognition-with-dlib/), [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/). While Deepface handles all these common stages in the background, you don’t need to acquire in-depth knowledge about all the processes behind it. You can just call its verification, find or analysis function with a single line of code. **Face Verification** - [`Demo`](https://youtu.be/KRCvkNCOphE) This function verifies face pairs as same person or different persons. It expects exact image paths as inputs. Passing numpy or base64 encoded images is also welcome. Then, it is going to return a dictionary and you should check just its verified key. ```python result = DeepFace.verify(img1_path = "img1.jpg", img2_path = "img2.jpg") ``` <p align="center"><img src="https://raw.githubusercontent.com/serengil/deepface/master/icon/stock-1.jpg" width="95%" height="95%"></p> **Face recognition** - [`Demo`](https://youtu.be/Hrjp-EStM_s) [Face recognition](https://sefiks.com/2020/05/25/large-scale-face-recognition-for-deep-learning/) requires applying face verification many times. Herein, deepface has an out-of-the-box find function to handle this action. It's going to look for the identity of input image in the database path and it will return pandas data frame as output. ```python df = DeepFace.find(img_path = "img1.jpg", db_path = "C:/workspace/my_db") ``` <p align="center"><img src="https://raw.githubusercontent.com/serengil/deepface/master/icon/stock-6-v2.jpg" width="95%" height="95%"></p> **Embeddings** Face recognition models basically represent facial images as multi-dimensional vectors. Sometimes, you need those embedding vectors directly. DeepFace comes with a dedicated representation function. ```python embedding = DeepFace.represent(img_path = "img.jpg") ``` This function returns an array as output. The size of the output array would be different based on the model name. For instance, VGG-Face is the default model for deepface and it represents facial images as 2622 dimensional vectors. ```python assert isinstance(embedding, list) assert model_name = "VGG-Face" and len(embedding) == 2622 ``` Here, embedding is also [plotted](https://sefiks.com/2020/05/01/a-gentle-introduction-to-face-recognition-in-deep-learning/) with 2622 slots horizontally. Each slot is corresponding to a dimension value in the embedding vector and dimension value is explained in the colorbar on the right. Similar to 2D barcodes, vertical dimension stores no information in the illustration. <p align="center"><img src="https://raw.githubusercontent.com/serengil/deepface/master/icon/embedding.jpg" width="95%" height="95%"></p> **Face recognition models** - [`Demo`](https://youtu.be/i_MOwvhbLdI) Deepface is a **hybrid** face recognition package. It currently wraps many **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/), [`ArcFace`](https://sefiks.com/2020/12/14/deep-face-recognition-with-arcface-in-keras-and-python/), [`Dlib`](https://sefiks.com/2020/07/11/face-recognition-with-dlib-in-python/) and `SFace`. The default configuration uses VGG-Face model. ```python models = [ "VGG-Face", "Facenet", "Facenet512", "OpenFace", "DeepFace", "DeepID", "ArcFace", "Dlib", "SFace", ] #face verification result = DeepFace.verify(img1_path = "img1.jpg", img2_path = "img2.jpg", model_name = models[1] ) #face recognition df = DeepFace.find(img_path = "img1.jpg", d