Face Detection Using Mixture of MLP Experts

Authors:
Reza Ebrahimpour;Ehsanollah Kabir;Mohammad Reza Yousefi
Affiliations:
School of Cognitive Sciences, Institute for Studies on Theoretical Physics and Mathematics, Tehran, Iran and Department of Electrical Engineering, Shahid Rajaee University, Tehran, Iran;Department of Electrical Engineering, Tarbiat Modarres University, Tehran, Iran;Department of Electrical Engineering, Shahid Rajaee University, Tehran, Iran
Venue:
Neural Processing Letters
Year:
2007

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Abstract

This paper presents a face detection method which makes use of a modified mixture of experts. In order to improve the face detection accuracy, a novel structure is introduced which uses the multilayer perceptrons (MLPs), as expert and gating networks, and employs a new learning algorithm to adapt with the MLPs. We call this model Mixture of MLP Experts (MMLPE). Experiments using images from the CMU-130 test set demonstrate the robustness of our method in detecting faces with wide variations in pose, facial expression, illumination, and complex backgrounds. The MMLPE produces promising high detection rate of 98.8% with ten false positives.