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Robust in-plane and out-of-plane face detection algorithm using frontal face detector and symmetry extension

机译：使用正面人脸检测器和对称扩展的鲁棒面内和面外人脸检测算法

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Face detection plays an important role in many computer vision applications. In recent years, much research has focused on extending the well-established Adaboost face detector algorithm for multi-view face detection. However, detecting in-plane and out-of-plane rotated faces simultaneously is still a challenging task today. In this paper, a very robust multi-view face detection algorithm, with its core functionality based on frontal face detection, is proposed to simultaneously detect in-plane and out-of-plane rotated faces. Moreover, only the training data in the frontal face is needed and we do not require the training data from different in-plane or out-of-plane rotation angles. In the proposed algorithm, first, techniques such as the skin filter and entropy rate superpixels (ERSs) are applied to obtain face candidates. Then, angle compensation and refinement are applied to improve the accuracy of face detection in the in-plane case. Moreover, the symmetry extension technique, i.e., extending the face candidate with its flipped version to create a face similar to the frontal one, is applied to detect out-of-plane faces without the need of training data. Simulations on the FEI, Pointing'04, BaoFace, Group, Utrecht, and WWW datasets demonstrate the proposed algorithm's effectiveness and superior performance as compared to state-of-the-art face detection methods. (C) 2018 Elsevier B.V. All rights reserved.
机译：人脸检测在许多计算机视觉应用中起着重要作用。近年来，许多研究集中在扩展成熟的Adaboost面部检测器算法以进行多视图面部检测。但是，如今，同时检测平面内和平面外旋转面仍然是一项艰巨的任务。本文提出了一种非常健壮的多视角人脸检测算法，该算法具有基于正面人脸检测的核心功能，可以同时检测面内和面外旋转脸。此外，仅需要正面的训练数据，而我们不需要来自不同平面内或平面外旋转角度的训练数据。在提出的算法中，首先，应用诸如皮肤过滤器和熵速率超像素（ERS）之类的技术来获取人脸候选对象。然后，应用角度补偿和细化以提高面内情况下的面部检测精度。此外，对称扩展技术，即用其翻转版本扩展人脸候选以创建类似于额脸的人脸，被应用于检测平面外人脸，而无需训练数据。在FEI，Pointing'04，BaoFace，Group，Utrecht和WWW数据集上的仿真表明，与最新的人脸检测方法相比，该算法的有效性和优越的性能。 （C）2018 Elsevier B.V.保留所有权利。

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