How do you handle face detection in complex and dynamic scenes such as crowds, videos, and social media?

One weird trick you can consider using when dealing with videos is finding a way to add a 4th channel into your input image which indicates where the faces were detected in the previous frame. Arguably, the next frame in a sequence shouldn't be *that much* different from the previous one and the number and location of faces will pretty much be in the same general area as the previous frame.

Pre-process the image dataset, using any suitable pre-processing tool, I prefer to use open-source tools, use methods like image normalization, resizing, and enhancement to improve the quality of input images. High-quality annotated datasets are crucial for training robust models, so I'd invest time in creating comprehensive datasets with a wide variety of scenarios, including crowded scenes and different social media contexts. Data augmentation techniques such as random cropping, rotation, and scaling can further enhance the model's ability to generalize to new and unseen conditions.

One of the main hyper-parameters when dealing with challenging problems such as face detection is the use of an appropriate loss function during training. For example, one should consider using Focal Loss, which helps the model learn the hard to classify examples. https://medium.com/visionwizard/understanding-focal-loss-a-quick-read-b914422913e7

Use accuracy, precision, recall, metrics, and F1-score. Implementing confusion matrices to visualize true positives, true negatives, false positives, and false negatives. Additionally, Receiver Operating Characteristic (ROC) curves and Area Under the Curve (AUC) can be used for evaluating performance. In Python, libraries like Scikit-learn provide these evaluation tools. Also, cross-validation to ensure robustness and consider using Grad-CAM for visualizing model focuses on images, enhancing interpretability and debugging.