Multi-view face detection using frontal face detector

The development of frontal face detection has matured for application to everyday life. However, various profile face views are still difficult to deal with practically. A common solution is to build different detectors organized by a decision tree and ensure that each detector handles a single view or a few views. However, collecting the training images for each face view is time consuming and laborious. Moreover, many profile face detectors cannot perform as well as the frontal face detector because of insufficient training images. The management of many detectors also leads to too complex a logic structure to classify a face into its corresponding view. In this paper,we propose a novel method to reuse a frontal face detector to detect multi-view faces, which do not need any data collection or training processes for various profile views. We focus on exploiting the potential of a general frontal face detector and aim to extend its application to multi-view faces. We conduct a theoretical analysis to explain why our methodology works from different perspectives, and implement the proposed method based on the sliding window strategy. Furthermore, the searching process is optimized by a genetic algorithm with an original fitness function. Experimental results verify that our method can successfully detect human faces in almost all head poses in the dataset containing a complete collection of head poses in yaw and pitch axes. (c) 2018 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.