FaceCamera

Introduction

FaceCamera is a face recognition system designed to facilitate real-time identity verification for logging into a webpage based on employee status. Using a combination of Cascade Classifier for face detection, FaceNet for feature extraction, and Artificial Neural Network (ANN) for classification, the system provides accurate and efficient face recognition capabilities. As well as detecting using smiles and head movements. The language used is python with django admin database.

Meet The Team

NIM	Name	University	Scope of Task
17210809	Roslina Puspita	Universitas Bina Sarana Informatika	Model Research, Dataset Collection, Data Processing, Deep Learning Model Development, Model Training, Testing & Optimization, Model Deployment, Real-Time Face Recognition, Model Training, ML-Frontend Integration, ML Backend, Frontend & Backend initial view, ML for Smile and Head Movement, Camera, Documentation
17210640	Lailatul Qodariyah	Universitas Bina Sarana Informatika	Model Research, Dataset Collection, Refine the initial look of the Backend, ML Backend, Backend creation of enhancements that have been made
19210759	Syifa Rahma Leily	Universitas Bina Sarana Informatika	Part of the Backend, Create a login on the backend that only supervisors can enter
19210782	Fransisca Kusuma	Universitas Bina Sarana Informatika	Creating Frontend Views using Frames and BI Logos
15210380	Raihan Juniargho	Universitas Bina Sarana Informatika	Creating Frontend Views using Frames and BI Logos

Flow Diagram for Face Detection and Recognition

The following diagram illustrates the step-by-step process for face detection and recognition using MTCNN to detect faces, FaceNet to extract facial features, and ANN to classify faces as well as detection using smiles and head movements :

Requirements

To run this project, you need the following Python packages with their specified versions :

• absl-py==2.1.0
• asgiref==3.8.1
• astunparse==1.6.3
• blinker==1.8.2
• certifi==2024.8.30
• charset-normalizer==3.4.0
• click==8.1.7
• colorama==0.4.6
• Django==5.1.2
• Flask==3.0.3
• flatbuffers==24.3.25
• gast==0.6.0
• google-pasta==0.2.0
• grpcio==1.67.0
• h5py==3.12.1
• idna==3.10
• itsdangerous==2.2.0
• Jinja2==3.1.4
• joblib==1.4.2
• keras==3.6.0
• keras-facenet==0.3.2
• libclang==18.1.1
• lz4==4.3.3
• Markdown==3.7
• markdown-it-py==3.0.0
• MarkupSafe==3.0.2
• mdurl==0.1.2
• ml-dtypes==0.4.1
• mtcnn==1.0.0
• namex==0.0.8
• numpy==2.0.2
• opencv-python==4.10.0.84
• opt_einsum==3.4.0
• optree==0.13.0
• packaging==24.1
• pillow==11.0.0
• protobuf==5.28.3
• Pygments==2.18.0
• requests==2.32.3
• rich==13.9.3
• scikit-learn==1.5.2
• scipy==1.14.1
• six==1.16.0
• sqlparse==0.5.1
• tensorboard==2.18.0
• tensorboard-data-server==0.7.2
• tensorflow==2.18.0
• tensorflow-io-gcs-filesystem==0.31.0
• tensorflow_intel==2.18.0
• termcolor==2.5.0
• threadpoolctl==3.5.0
• typing_extensions==4.12.2
• tzdata==2024.2
• urllib3==2.2.3
• Werkzeug==3.0.5
• wrapt==1.16.0

This Project Allow :

• Python 3.10.5

Check the following installation if there is no please install this :

• flask
• cv2
• face_recognition
• numpy

Installation

To set up the project locally, follow these steps: (Create a new folder with the name FaceCamera and clone the project in this directory)

1. Clone the repository:

   git clone https://github.com/BI-Presence/FaceCamera.git
   cd FaceCamera

2. Create a virtual environment:

   python -m venv my_env

3. Activate the virtual environment:

   my_env\Scripts\activate

4. Install the required dependencies:

   pip install -r requirements.txt

5. Install other package in description :

   • flask
   • cv2
   • face_recognition
   • numpy

6. Run Camera:

   cd FrontEnd
   python camera.py

7. Access the application: Open your web browser and go to http://127.0.0.1:8000

Folder:

• The Try folder contains ML Code on the Front End and also the Frontend Camera
• AppBack folder is a folder that contains the Backend in the project created both ML and also the Backend View