3 February 2014 A Viola-Jones based hybrid face detection framework
Author Affiliations +
Improvements in face detection performance would benefit many applications. The OpenCV library implements a standard solution, the Viola-Jones detector, with a statistically boosted rejection cascade of binary classifiers. Empirical evidence has shown that Viola-Jones underdetects in some instances. This research shows that a truncated cascade augmented by a neural network could recover these undetected faces. A hybrid framework is constructed, with a truncated Viola-Jones cascade followed by an artificial neural network, used to refine the face decision. Optimally, a truncation stage that captured all faces and allowed the neural network to remove the false alarms is selected. A feedforward backpropagation network with one hidden layer is trained to discriminate faces based upon the thresholding (detection) values of intermediate stages of the full rejection cascade. A clustering algorithm is used as a precursor to the neural network, to group significant overlappings. Evaluated on the CMU/VASC Image Database, comparison with an unmodified OpenCV approach shows: (1) a 37% increase in detection rates if constrained by the requirement of no increase in false alarms, (2) a 48% increase in detection rates if some additional false alarms are tolerated, and (3) an 82% reduction in false alarms with no reduction in detection rates. These results demonstrate improved face detection and could address the need for such improvement in various applications.
© (2014) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Thomas M. Murphy, Thomas M. Murphy, Randy Broussard, Randy Broussard, Robert Schultz, Robert Schultz, Ryan Rakvic, Ryan Rakvic, Hau Ngo, Hau Ngo, "A Viola-Jones based hybrid face detection framework", Proc. SPIE 9025, Intelligent Robots and Computer Vision XXXI: Algorithms and Techniques, 902509 (3 February 2014); doi: 10.1117/12.2040829; https://doi.org/10.1117/12.2040829

Back to Top