  In last few decades, a lot of progress has been made in the field of face
detection. Various face detection methods have been proposed by numerous
researchers working in this area. The two well-known benchmarking platform: the
FDDB and WIDER face detection provide quite challenging scenarios to assess the
efficacy of the detection methods. These benchmarking data sets are mostly
created using images from the public network ie. the Internet. A recent, face
detection and open-set recognition challenge has shown that those same face
detection algorithms produce high false alarms for images taken in surveillance
scenario. This shows the difficult nature of the surveillance environment. Our
proposed body pose based face detection method was one of the top performers in
this competition. In this paper, we perform a comparative performance analysis
of some of the well known face detection methods including the few used in that
competition, and, compare them to our proposed body pose based face detection
method. Experiment results show that, our proposed method that leverages body
information to detect faces, is the most realistic approach in terms of
accuracy, false alarms and average detection time, when surveillance scenario
is in consideration.
