The paper introduces a few major issues with image face detection and presents some solutions to tackle them.
It sets out with a description of the FDDB system as a means of evaluation and comparison of different approaches in this field.
It furthermore examines some freely available implementations of academic methods and free versions of commercial solutions.
Among the academic methods the paper concentrates on analysing the Viola-Jones object detection framework along with different implementations of the detection cascades based on Haar, LBP and SURF features.
In addition, it describes solutions which use the DPM, HOG pyramids with a SVM classifier and pixel-intensity classifier.
The selected commercial solutions presented in the paper include Face++ (which utilizes deep learning and neural network algorithms), BetaFace, MS Project Oxford and Apple Photos (which do not publicly reveal the method of detection).
Finally, the paper concludes with establishing that a careful parameter selection of open-source solutions can bring about at least comparable results to those achieved with the commercial solutions.
The comparison of detection time determines pico as the fastest method that still yields useful results while the HOG method implemented with dlib and the DPM method implemented with voc-dpm are found to give the most accurate results.