https://github.com/canxkoz/multiple-face-recognition

Multipe Face Recognition program that uses Keras and OpenCV.
https://github.com/canxkoz/multiple-face-recognition

face face-detection face-recognition multiple-faces

Last synced: 12 months ago
JSON representation

Multipe Face Recognition program that uses Keras and OpenCV.

• Host: GitHub
• URL: https://github.com/canxkoz/multiple-face-recognition
• Owner: canxkoz
• License: mit
• Created: 2019-04-28T02:07:49.000Z (almost 7 years ago)
• Default Branch: master
• Last Pushed: 2019-06-04T20:40:40.000Z (almost 7 years ago)
• Last Synced: 2025-04-01T18:50:13.653Z (about 1 year ago)
• Topics: face, face-detection, face-recognition, multiple-faces
• Language: Python
• Size: 5.78 MB
• Stars: 15
• Watchers: 2
• Forks: 5
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

README

          
# Multiple-Face-Recognition

Multipe Face Recognition program that uses Keras and OpenCV. I built this as a part of DVCHacks.

![Testing Image](pictures_for_readme/Test.jpg)

## build_dataset.py

Taking pictures of the user by using OpenCV. Saving those pictures in a folder named "dataset". By the use of haarcascade features the pictures only include human faces.

![Creating a Dataset](pictures_for_readme/Build_Dataset.JPG)

## train_data.py

I created a convolutional feature extractor network with multiple layers. I did that in order to genereate a representation vector of the input images which will make use of "dataset". 

- Softmax is used in this project as a last layer. output activation function. 

- The training is done by the use of the Adam optimizer function. 

- The learning rate of the Adam optimizer is 3e-4. 

- As a loss function I used binary crossentropy, the reason why I preferred binary crossentopy is becasue there were two classes. 

- For future work if you want to add more classes you may use categorical crossentropy function. 

- The validation set is chosen as 10% of the training set.

- The traninig of my model is complteted within 30 epochs. 

- Validation accuracy, validation loss of the model is printed at the end of the training process. 

- At last weights are saved as a ".h5" file and model structure is saved as a ".yaml" file and both of them are kept in "keras_model" folder.  

![Training the Model](pictures_for_readme/Train_Data.JPG)

## check_result.py

The test accuracy can be seen from the results of this file. In this file, I crate labels for the test set.

![Creating a Dataset](pictures_for_readme/Check_Result.JPG)

## real_time_re.py

Real time test of the model from the webcam of your setup.

References

- [Keras](https://keras.io/)

- [OpenCV](https://opencv.org/)

- [TensorFlow](https://www.tensorflow.org/)