https://github.com/paoloripamonti/face-recognition

Face Recognition
https://github.com/paoloripamonti/face-recognition

deep-learning face-alignement face-detection face-encoding face-recognition machine-learning

Last synced: about 2 months ago
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Face Recognition

• Host: GitHub
• URL: https://github.com/paoloripamonti/face-recognition
• Owner: paoloripamonti
• Created: 2019-09-26T08:39:11.000Z (over 5 years ago)
• Default Branch: master
• Last Pushed: 2023-11-19T12:42:57.000Z (about 1 year ago)
• Last Synced: 2024-11-06T11:52:16.469Z (about 2 months ago)
• Topics: deep-learning, face-alignement, face-detection, face-encoding, face-recognition, machine-learning
• Language: Python
• Size: 1.51 MB
• Stars: 15
• Watchers: 3
• Forks: 12
• Open Issues: 5
• Metadata Files:
  • Readme: README.md

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README

        
# Face Recognition

Simple library to recognize faces from given images

[](https://www.kaggle.com/paoloripamonti/face-recogniton)

### Face Recognition pipeline

Below the pipeline for face recognition:

- **Face Detection**: the [MTCNN](https://github.com/ipazc/mtcnn) algorithm is used to do face detection

- **Face Alignement** Align face by eyes line

- **Face Encoding** Extract encoding from face using [FaceNet](https://github.com/faustomorales/keras-facenet)

- **Face Classification** Classify face via eculidean distrances between face encodings

### How to install

```git

pip install git+https://github.com/paoloripamonti/face-recognition

```

### How to train custom model

Initialize model

```python

from face_recognition import FaceRecognition

fr = FaceRecognition()

```

#### Train model with pandas DataFrame:

```python

fr = FaceRecognition()

fr.fit_from_dataframe(df)

```

where 'df' is pandas DataFrame with column **person** (person name) and column **path** (image path)

#### Train model with folder:

```python

fr = FaceRecognition()

fr.fit('/path/root/')

```

the root folder must have the following structure:

```

root\

    Person_1\

        image.jpg

        ...

        image.jpg

    Person_2\

        image.jpg

        ...

        image.jpg

    ...

        

```

### Save and load model

you can save and load model as **pickle** file.

```python

fr.save('model.pkl')

```

```python

fr = FaceRecognition()

fr.load('model.pkl')

```

### Predict image

```python

fr.predict('/path/image.jpg')

```

Recognize faces from given image.

The output is a JSON with folling structure:

```

{

  "frame": "image data", # base64 image with bounding boxes

  "elapsed_time": time, # elapsed time in seconds

  "predictions": [

      {

        "person": "Person", # person name

        "confidence": float, # prediction confidence

        "box": (x1, y1, x2, y2) # face bounding box

      }

  ]

}

```

### Example

[](https://www.kaggle.com/paoloripamonti/face-recogniton)

[](https://www.kaggle.com/paoloripamonti/face-recogniton)

For more details you can see: [https://www.kaggle.com/paoloripamonti/face-recogniton](https://www.kaggle.com/paoloripamonti/face-recogniton)