auto-face-recognition 0.0.1

auto_face_recognition is Tensorflow based python library for fast face recognition

auto_face_recognition

Last Upadted: 02 September, 2020

1. What is auto_face_recognition?
2. Prerequisite
3. Getting Started- How to use it?
4. Future?

1. What is auto_face_recognition?

It is a python library for the Face Recognition. This library make face recognition easy and simple. This library uses Tensorflow 2.0+ for the face recognition and model training.

2. Prerequisite-

• To use it only Python (> 3.6) is required.

3. Getting Started (How to use it)-

Install the latest version-

pip install auto_face_recognition

It will install all the required package automatically, including Tensorflow Latest.

Usage and Features-

After installing the library you can import the module-

1. Object Creation-

   import auto_face_recognition
   obj = auto_face_recognition.AutoFaceRecognition()
   

2. Dataset Creation-

    obj.datasetcreate(haarcascade_path='haarcascade/haarcascade_frontalface_default.xml',  
    	    	                  eyecascade_path='haarcascade/haarcascade_eye.xml') 
   

   Note: You need to pass the 'haarcascade_frontalface_default.xml' and 'haarcascade_eye.xml' path.

3. Model Training-

    obj.face_recognition_train()		
   

4. Predict Faces-

    obj.predict_faces()
   

Parameters You Can Choose-

datasetcreate

datasetcreate(dataset_path='datasets', class_name='Demo',  
                  haarcascade_path='haarcascade/haarcascade_frontalface_default.xml',  
                  eyecascade_path='haarcascade/haarcascade_eye.xml', eye_detect=False,  
                  save_face_only=True, no_of_samples=100,  
                  width=128, height=128, color_mode=False)
""""                  
Dataset Create by face detection  
:param dataset_path: str (example: 'folder_of_dataset')
:param class_name: str (example: 'folder_of_dataset')
:param haarcascade_path: str (example: 'haarcascade_frontalface_default.xml)
:param eyecascade_path: str (example: 'haarcascade_eye.xml):param eye_detect: bool (example:True)
:param save_face_only: bool (example:True)
:param no_of_samples: int (example: 100)
:param width: int (example: 128)
:param height: int (example: 128)
:param color_mode: bool (example:False):return: None
"""  

face_recognition_train

face_recognition_train(data_dir='datasets', batch_size=32, img_height=128, img_width=128, epochs=10,  
                           model_path='model'):  
 """  
 Train TF Keras model according to dataset path  
 :param data_dir: str (example: 'folder_of_dataset')  
 :param batch_size: int (example:32)  
 :param img_height: int (example:128)  
 :param img_width: int (example:128)  
 :param epochs: int (example:10)  
 :param model_path: str (example: 'model')  
 :return: None  
 """

predict_faces

predict_faces(self, class_name=None, img_height=128, img_width=128,  
              haarcascade_path='haarcascade/haarcascade_frontalface_default.xml',  
              eyecascade_path='haarcascade/haarcascade_eye.xml', model_path='model',  
              color_mode=False):  
 """  
 Predict Face  
 :param class_name: Type-List (example: ['class1', 'class2'] )  
 :param img_height: int (example:128)  
 :param img_width: int (example:128)  
 :param haarcascade_path: str (example: 'haarcascade_frontalface_default.xml)  
 :param eyecascade_path: str (example: 'haarcascade_eye.xml)  
 :param model_path: str (example: 'model')  
 :param color_mode: bool (example: False)  
 :return: None  
 """

4. Future?

Finetuning with Resnet and others.
You Suggest.

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