  Detecting faces and heads appearing in video feeds are challenging tasks in
real-world video surveillance applications due to variations in appearance,
occlusions and complex backgrounds. Recently, several CNN architectures have
been proposed to increase the accuracy of detectors, although their
computational complexity can be an issue, especially for real-time
applications, where faces and heads must be detected live using high-resolution
cameras. This paper compares the accuracy and complexity of state-of-the-art
CNN architectures that are suitable for face and head detection. Single pass
and region-based architectures are reviewed and compared empirically to
baseline techniques according to accuracy and to time and memory complexity on
images from several challenging datasets. The viability of these architectures
is analyzed with real-time video surveillance applications in mind. Results
suggest that, although CNN architectures can achieve a very high level of
accuracy compared to traditional detectors, their computational cost can
represent a limitation for many practical real-time applications.
