Real-Time Face Recognition Using AlexNet-Based Transfer Learning and Stochastic Gradient Descent with Momentum Optimizer
A MATLAB program that detects and recognises faces through the live webcam.
| Name | Description |
|---|---|
| AuthorImages | Images of authors/contributors of this image processing group project. |
| Faces | A root folder for face images. This folder will be automatically created during the process of taking face images. |
| Faces[sub-folder-name] | A sub-folder inside the 'Faces' folder to store/save the collected face images. I.e., 'Faces\Your Name' will store all your face images and 'Faces\Name 2' will store another person's face images. |
| Icons | Images of icon to decorate the buttons. |
| TestCode | All the source codes before merging into the MATLAB App Designer. |
| TestCode\collect_faces.m | Open webcam and capture face images. |
| TestCode\examine_network_layers.mlx | A live script to examine the AlexNet layers. |
| TestCode\test.m | Run this file to test your trained network with the live webcam. |
| TestCode\train.m | Run this file to train your network. |
| TestCode\trainfaces.mlx | A live script to test the training result. |
| TestCode\trainfaces_ex2.mlx | Another live script to test the training result. |
| facenet.mat | The newly trained network (Not provided in this file as you need to train and generate your own network). |
| credits.mlapp | Build the credits UI in MATLAB App Designer. |
| creditsGUI.m | Exported from credits.mlapp |
| faceRecognition.mlapp | Build the application UI in MATLAB App Designer. |
| faceRecognitionGUI.m | Exported from faceRecognitionGUI.mlapp |
| license.txt | A license document. |
- Run the faceRecognitionGUI.m in MATLAB.
- Click the Capture Face Image button to capture 500 face images (You will get 0 - 499 images). The face images are saved in a separate folder under the root folder Faces.
- Repeat Step 2 if you are collecting more face images (more than 1 person).
- Click the Train Model button to train your network. Wait for this process to finish.
- If you are satisfied with the training result, click the Begin Face Recognition button to test your newly trained network.
- If you would like to retrain the network, repeat Step 2 - Step 5. (Note: facenet.mat will be OVERWRITTEN, SAVE the file in somewhere else before proceeding).
- Click the File > Exit to exit the application/program.
7/22/2022
- Updated folders and files.
- Release v0.1.0 Beta.
5/10/2022
- Start the project.
- Release v0.0.1.
| No. | Website |
|---|---|
| 1. | Complete Face Recognition Project Using MATLAB by Knowledge Amplifier |
| 2. | Deep Learning Onramp |
| 3. | Acquiring a Single Image in a Loop |
| 4. | Determining the Rate of Acquisition |