ehtz / deepface-gui

Simple DeepFace GUI - DeepFind

Simple DeepFace GUI by Ehtz

This is a simple graphical user interface (GUI) for DeepFace, a deep learning framework for facial recognition. The GUI allows you to find similar faces in a database using various face recognition models. Here's how to use it:

Getting Started

1. Ensure you have Python installed on your system.

2. Clone this repository or download the deepface_gui.py file to your local machine.

3. Install the required Python libraries by running the following command: pip install deepface pandas

Running the Application

1. Open a terminal or command prompt.

2. Navigate to the directory where the deepface_gui.py file is located.

3. Run the application by executing the following command: python deepface_gui.py

4. The GUI application window will open.

How to Use

• Database Images Folder: Click the "Select Folder" button to choose a folder containing your database images. This is the collection of images you want to search for similar faces in.

• Base Image: Click the "Select Image" button to choose the image you want to find similar faces for. You can also click "Open Base Image" to view the selected image.

• Select Model: Choose a face recognition model from the dropdown menu. The default is "Facenet512," but you can select other models provided in the list.

• Click the "Find Similar Faces" button to start the face recognition process.

• The results will be displayed in the text area below. You will see a list of similar faces found in the database, along with similarity scores for the selected model.

• Use the scrollbar on the right to scroll through the results.

Important Notes

• This application uses DeepFace for face recognition. Make sure to provide a database folder with images and a base image for comparison.

• The accuracy of the results depends on the selected model and the quality of the images in your database.

• If you encounter any errors or issues, please make sure you have correctly installed the required libraries and provided valid file paths.

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(Screen shot taken of the GUI)

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Only used the DeepFace.find command:

dfs = DeepFace.find(img2_path, db_path=db_path, model_name=selected_model, enforce_detection=False)

Added Drop Down - Chose between certain models FaceNet, VGG-Face, ArcFace and Dlib are overperforming ones based on experiments. You can find out the scores of those models below on both Labeled Faces in the Wild and YouTube Faces in the Wild data sets declared by its creators.

Model	LFW Score	YTF Score
Facenet512	99.65%	-
SFace	99.60%	-
ArcFace	99.41%	-
Dlib	99.38%	-
Facenet	99.20%	-
VGG-Face	98.78%	97.40%
Human-beings	97.53%	-
OpenFace	93.80%	-
DeepID	-	97.05%

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Enjoy using this Simple DeepFace GUI!

Author: Ehtz

GitHub Repository: link

Link towards the main DeepFace project: link

Please report any issues or suggestions on the GitHub repository.