Question 1

How do I compile libfacedetection on a Raspberry Pi?

Accepted Answer

Enable NEON optimizations for ARM and use g++ with the -O3 flag; for multi-threading, you can use OpenMP or call the detection function in separate threads as recommended in the README tips.

Question 2

What's the difference between libfacedetection and OpenCV's face detector?

Accepted Answer

libfacedetection uses a CNN model for higher accuracy, especially in challenging conditions, and is optimized for speed with SIMD, while OpenCV offers cascades that might be faster on low-end hardware but less accurate. Integration with OpenCV DNN has limitations noted in the README.

Question 3

Can I use libfacedetection with Python instead of C++?

Accepted Answer

Direct Python bindings are not provided; you need C++ integration or can use the Python scripts in the opencv_dnn directory with the ONNX model, but this offers limited functionality and requires OpenCV.

Question 4

How accurate is libfacedetection compared to modern deep learning models?

Accepted Answer

It achieves competitive AP scores on WIDER Face, but for state-of-the-art accuracy in complex scenarios, heavier models like RetinaFace might be better, though at the cost of speed and resource usage.

Question 5

How to enable multi-threading to boost FPS in libfacedetection?

Accepted Answer

Use OpenMP during compilation or implement application-level threading by calling the detection function concurrently in different threads, as suggested in the README for optimal performance scaling.

Question 6

Does libfacedetection support GPU inference for faster detection?

Accepted Answer

No, it's optimized for CPU with SIMD instructions; for GPU acceleration, you would need to port the model or use other libraries like TensorRT with the provided ONNX model, which adds complexity.

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