The system performs real-time facial recognition by extracting unique facial features from a given image using Transfer Learning with VGGFace model, a state-of-the-art deep learning model for facial feature extraction. By comparing these extracted features with the existing ones stored in a database of pre-registered individuals, The system determines whether the detected person is recognized (registered) or not. If recognized, it retrieves and displays their informations. I also used Kafka and Spark for the data processing hence the expression " Real Time"
- You can either choose a pictures from your pc or open your camera and take a new one .
- The input image is hashed and send throught a Kafka topic which Spark listens to .
- Flask serves as the backend to handle HTTP requests, process image inputs, and communicate with the model.
- The VGGFace model (pre-trained on a large facial dataset) is used to extract unique facial features .
- After Spark Consumed the input image from kafka topic, Flask processes the image and passes it to the model for generating a feature vector (embedding), which numerically represents the face.
- The output of the model (features extracted) is then sent back throught another kafka topic which i configure spring boot app to wait for it's return which is the features obviously.
- The extracted feature vector is compared against existing embeddings stored in a CSV database.
- Each record in the database corresponds to a registered person including Their facial feature embedding (pre-calculated and stored), Metadata like name, age, gender, and profession.
- The cosine similarity metric is used to compare the feature vector : A high cosine similarity (above a set threshold, e.g., 0.75) indicates a match. If no match is found, the system labels the individual as unregistered.
1. Make sure first you installed Spark ,Kafka :
Spark (3.5.1) , Kafka (3.7.1) : Those versions worked for me
2. Important !! : After you clone the repo, move the Hadoop Directory i have put in this repo , under your 'C:\' for example .
3. Add Spark, kafka, Hadoop correctly to your environment variables
- Clone the project
git clone https://github.com/Marouansw/FaceReco--RTSS.git- Go to the project directory
cd FaceReco--RTSS- Create new python venv
python -m venv venv- Activate it
venv\Scripts\activate- Install requirements.txt
pip install -r requirements.txt- Go to Model Directory and open the features_model.ipynb in VScode for example, then choose the venv as a Kernel to run your code
--> Now, the model is hosted Locally on port 5000
- Just After this, Open a new Terminal and cd to your kafka Directory, in my case :
cd C:\kafka - Start the Zookeeper services :
bin\windows\zookeeper-server-start.bat config\zookeeper.properties- Open a new Terminal again and cd to your kafka Directory, and start Kafka services :
bin\windows\kafka-server-start.bat config\server.properties- Open a new Terminal and cd to Spark_streaming directory of the repo, it should by something like this :
cd C:\Users\LENOVO\Desktop\Master DS\Deep Learning\FaceReco--RTSS\Spark_streaming- Lunch Spark_streaming.py, replace with your python exe, in my case :
spark-submit --packages org.apache.spark:spark-sql-kafka-0-10_2.12:3.5.1,org.apache.kafka:kafka-clients:3.5.1 --conf "spark.pyspark.python=C:\Users\LENOVO\AppData\Local\Programs\Python\Python310\python.exe" spark_streaming.py- One more thing left, assuming you already opened the project in vscode, in terminal, Run Spring boot app :
mvn clean install mvn spring-boot:run