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This repository is home to an exploration in the field of Facial Recognition using Convolutional Neural Networks. It is based on the performance comparison between different models such as ResNet50, MobileNetV3, InceptionV3, EfficientNet, and VGG16. The models were trained using two types of losses - Triplet Loss and Categorical Cross Entropy.

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TFG-Real_Time_Facial_Recognition

Introduction

This repository is the practical part of my Final Degree Project in Computer Engineering. It hosts an exploration of the field of facial recognition using deep learning. Inspired by a crossover interest in machine learning and the visual world, this project investigates the complexities of facial recognition, focusing on model performance, speed, and efficiency in real-time implementation.

Models and Techniques

We have compared between two types of loss functions and different models of the Convolutional Neural Network.

models = [
   "ResNet50",
  "MobileNetV3",
  "InceptionV3",
  "EfficientNet", 
  "VGG16"
]

loss_functions = [
   "Triplet Loss",
  "Categorical Cross Entropy",
]

face_detector_alignment_crop = ["MTCNN"]
face_detector_real_time = ["Media-pipe"]

Evaluation

Base Model Accuracy Precision Recall F1-Score
ResNet50 - Triplet Loss 97,78% 97,62% 97,78% 97,62%
ResNet50 - Categorical Cross Entropy 80% 82,86% 80,88% 80,02%
MobileNetV3 - Triplet Loss 91,11% 92,98% 92,16% 91,63%
MobileNetV3 - Categorical Cross Entropy 75,55% 77,73% 74,45% 74,74%
InceptionV3 - Triplet Loss 64,44% 64,01% 66,19% 64,41%
InceptionV3 - Categorical Cross Entropy 66,66% 70,36% 66,00% 66,97%
EfficientNet - Triplet Loss 95,55% 95,56% 95,82% 95,46%
VGG16 - Categorical Cross Entropy 84,44% 84,44% 84,81% 84,53%

Data Visualization

In the files triplet_loss_val.ipynb and cross_entropy_val.ipynb we can visualize the results obtained with the test dataset

  • Prediction on test dataset - ResNet50 - Triplet Loss

alt text

  • Confusion Matrix - ResNet50 - Triplet Loss

alt text

Real-time Deployment

To test models in real time we change the model path based on the missing function that we want to use in the real_time.ipynb file.

Below we have a sample of a recognition frame in real time with the model that gave us the best result (ResNet50 with the use of Triplet Loss):

alt text

References

About

This repository is home to an exploration in the field of Facial Recognition using Convolutional Neural Networks. It is based on the performance comparison between different models such as ResNet50, MobileNetV3, InceptionV3, EfficientNet, and VGG16. The models were trained using two types of losses - Triplet Loss and Categorical Cross Entropy.

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