I'm exploring different methods for face detection in a crowded environment. I've tried Haar Cascades, but the accuracy isn't great. Should I stick with the OpenCV DNN module using a pre-trained SSD model, or is there a more modern approach that handles occlusions better for real-world applications?
3 answers
Haar Cascades are quite dated now and prone to false positives in complex backgrounds. The OpenCV DNN module is far superior because it allows you to load models from TensorFlow or PyTorch directly. I recommend using the Caffe-based ResNet-10 model provided in the library's samples. It’s remarkably fast and much more robust against varying lighting conditions and facial angles compared to traditional methods. In my experience, even with slight occlusions like sunglasses or masks, the deep learning approach maintains a much higher confidence score, making it ideal for professional deployments.
Susan, do you find that the inference time for the ResNet model is low enough for edge devices like a Raspberry Pi, or does it require more powerful hardware?
For crowded scenes, definitely use the DNN module. It handles scale variations much better than the older XML-based classifiers which require manual parameter tuning.
Laura is right about scale; the multi-scale testing in SSD models is built-in. I’ve noticed that OpenCV also supports YOLO models now, which are excellent for detecting faces in very dense crowds.
Michael, for a Raspberry Pi, the ResNet model might be a bit heavy. In those cases, I usually suggest the MobileNet-SSD variant. It was specifically designed for mobile and embedded vision tasks. When used with OpenCV, it provides a perfect balance between speed and accuracy. If you use the OpenVINO toolkit for optimization on Intel hardware, you can get near real-time performance even on lower-end processors.