Highly Occluded Face Detection: An Improved R-FCN Approach

For highly occluded faces, only few features exist, which makes such face detection more challenging. In this paper, we propose a novel algorithm to make full use of the facial features. The proposed algorithm is based on Region-based Fully Convolutional Network (R-FCN) with two improved parts for robust face detection, including the multi-scale training and a new feature-fusion scheme. Firstly, instead of utilizing fixed scales for all faces, we adopt multi-scale inputs to strengthen the features of the partial faces and increase the training set diversity. Up-sampling the training images can efficiently enlarge the features of the occluded faces. Secondly, we make a feature fusion by combining layers with different sizes of receptive fields, which can preserve the details of the faces with only partial faces available. Our method achieves superior accuracy over the stat-of-the-art techniques on massively-benchmarked face dataset (WIDER FACE), and shows great improvements for highly occluded face detection.

[1]  Yi Li,et al.  R-FCN: Object Detection via Region-based Fully Convolutional Networks , 2016, NIPS.

[2]  Trevor Darrell,et al.  Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation , 2013, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[3]  Stan Z. Li,et al.  Learning representative local features for face detection , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[4]  Harry Shum,et al.  Kullback-Leibler boosting , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[5]  Paul A. Viola,et al.  Rapid object detection using a boosted cascade of simple features , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[6]  Yair Weiss,et al.  Learning object detection from a small number of examples: the importance of good features , 2004, CVPR 2004.

[7]  Trevor Darrell,et al.  Caffe: Convolutional Architecture for Fast Feature Embedding , 2014, ACM Multimedia.

[8]  Stefanos Zafeiriou,et al.  A survey on face detection in the wild: Past, present and future , 2015, Comput. Vis. Image Underst..

[9]  Bill Triggs,et al.  Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[10]  Gang Hua,et al.  A convolutional neural network cascade for face detection , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[11]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[12]  Xihong Wu,et al.  Boosting Local Binary Pattern (LBP)-Based Face Recognition , 2004, SINOBIOMETRICS.

[13]  Matti Pietikäinen,et al.  Face Recognition with Local Binary Patterns , 2004, ECCV.

[14]  Abhinav Gupta,et al.  Training Region-Based Object Detectors with Online Hard Example Mining , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[15]  Peiyun Hu,et al.  Finding Tiny Faces , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[16]  Qiang Ji,et al.  Multi-view face detection under complex scene based on combined SVMs , 2004, ICPR 2004.

[17]  Yizhou Wang,et al.  Face Detection with End-to-End Integration of a ConvNet and a 3D Model , 2016, ECCV.

[18]  Takeshi Mita,et al.  Joint Haar-like features for face detection , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[19]  Yu Qiao,et al.  Joint Face Detection and Alignment Using Multitask Cascaded Convolutional Networks , 2016, IEEE Signal Processing Letters.

[20]  Chengjun Liu,et al.  A Bayesian Discriminating Features Method for Face Detection , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[21]  Jean-Philippe Thiran,et al.  Face detection with boosted Gaussian features , 2007, Pattern Recognit..

[22]  Michael S. Bernstein,et al.  ImageNet Large Scale Visual Recognition Challenge , 2014, International Journal of Computer Vision.

[23]  Ross B. Girshick,et al.  Fast R-CNN , 2015, 1504.08083.

[24]  Shaogang Gong,et al.  Support vector regression and classification based multi-view face detection and recognition , 2000, Proceedings Fourth IEEE International Conference on Automatic Face and Gesture Recognition (Cat. No. PR00580).

[25]  Graham W. Taylor,et al.  Deconvolutional networks , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[26]  Shuo Yang,et al.  WIDER FACE: A Face Detection Benchmark , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).