Building a Robust Face Detection System: From Data Collection to Model Deployment

In this article, we'll explore the journey of implementing a face detection system, step by step. Let's dive in.

Note-: GitHub repo link -> https://github.com/Rhythm1821/Tensorflow-Face-Detection

Data Collection and Annotation:

The first step is collecting the data. Using OpenCV, a popular computer vision library, images were captured. These images then underwent annotation for face detection using labelme, which allows you to label your images for various tasks like object detection, image classification etc., enabling the drawing of bounding boxes around the faces to create labelled data.

Data Preprocessing

The annotated data was organized and stored in separate folders for images and labels. The (zip) data was then stored in a GitHub repository. Then I wrote a python script to access the zipped data and extract it. TensorFlow's data pipeline was employed, utilizing tf.data.Dataset and a custom data loading function. The images were loaded using TensorFlow's image decoding functions.

Data Augmentation and Scaling

To enhance model robustness, data augmentation was applied using Albumentations. This technique involves introducing variations in the images, like rotations, flips, and changes in brightness. The augmented data was stored separately from the original data.

Images were then scaled to the range of 0-1 by dividing by 255, a standard practice in neural network training.

Model Building:

For the core of the project, a deep learning model was developed. The architecture included a transfer learning technique where a pre-trained VGG16 model was employed as a base and additional Dense and Convolutional layers were added which made it a customized model. The model outputs were designed to handle both classification and regression tasks simultaneously.

Custom Loss Function

Incorporating a localization loss was crucial for the regression task of bounding box coordinates. The custom localization loss was defined to compute the differences in coordinates and sizes, contributing to the refinement of regression predictions.

Custom Model Class

The model was encapsulated within a custom class, FaceTracker, which extended TensorFlow's Model class. This class was designed to handle training and testing steps, compiling the model with BinaryCrossEntropy loss function and Adam optimizer, and implementing custom gradient updates during training.

Model Training and Evaluation

The model was trained for 50 epochs using the training dataset, and its performance was evaluated on the validation dataset. TensorBoard was utilized to monitor the training process, visualize losses, and track model performance over epochs.

Predictions and Model Deployment

After training, the model's predictions were tested on the test dataset. Impressively, the model demonstrated accurate predictions for real-world face detection scenarios. The trained model was saved in the .h5 format, ready for deployment.

Real-Time Face Detection:

Taking it a step further, a real-time face detection system was created using OpenCV. This system incorporated the trained model to detect faces in real-time video streams, showcasing the potential application of the developed model.

Conclusion

This project, focused on face detection, showcases how advancements in computer vision can be applied to real-world scenarios. The journey, from data collection and annotation to model deployment, was one with adventure. And I am looking forward to making more challenging and exciting projects.

Thank you for reading the article til the end :)