Implementation of Facenet in Matlab

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Ron Manto
Ron Manto il 29 Mag 2024
Risposto: Ninad il 29 Mag 2024
I'm trying to implement a system that's identifying faces in real-time - both of known (trained upon) individuals, and unknown persons. In addition, it should put a bounding box around them, with its accuracy score - similar to Facenet. Is there an example of this in Matlab ?
By identifying the known and unknown individuals, we aim at providing the total head-count in a given frame.
I am trying to then later use Matlab coder, to transfer this program to my Nvidia Jetson Platform, so DL models are preferable.

Risposte (1)

Ninad
Ninad il 29 Mag 2024
Hi Ron,
I've outlined a high-level approach below to assist with your project on face detection and recognition. Please consider this as a starting point, and feel free to adjust it according to your specific requirements.
  1. Face Detection:Consider using the Viola-Jones algorithm or HOG (Histogram of Oriented Gradients) features to detect faces within a live video stream. Initialize a list to store the coordinates of detected faces.For a practical example, you might find this real-time face recognition and detection system using PCA and the Viola-Jones algorithm helpful:https://www.mathworks.com/matlabcentral/fileexchange/46674-real-time-face-recognition-and-detection-system
  2. Face Recognition:Train a deep learning model, such as CNN (Convolutional Neural Network) or FaceNet, on a dataset of known faces. For each detected face, extract features and compare them with the dataset of known faces. If the similarity exceeds a certain threshold, label the face as recognized.Here's an example that demonstrates how to register and recognize a new face in real time:https://www.mathworks.com/matlabcentral/fileexchange/68985-real-time-facial-recognition-using-hog-features/
  3. Bounding Box and Accuracy Score:Utilize the coordinates of detected faces to draw bounding boxes around them. Calculate the accuracy of recognition by comparing detected features with those of known faces.
  4. Keep a total Head-Count:Maintain a count of detected faces. Increment the count for each new face.Following example shows the use of MATLAB to create a cloud based people counter:https://www.mathworks.com/matlabcentral/fileexchange/58435-creating-a-cloud-based-people-counter-using-matlab
  5. Use MATLAB Coder to generate C/C++ code.Optimize deep learning models for Jetson (e.g., TensorRT).
Please note that the suggestions provided are meant to serve as a general guide. I encourage you to explore the attached links and tailor the algorithm to better suit your project's needs.
Regards,
Ninad

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