An improved multi-scale face detection using convolutional neural network

In this paper, we introduce a deep learning (CNN) based method for face detection in an uncontrolled environment. The proposed method consists in developing a CNN architecture dedicated to the face detection tasks by combining both of global and local features at multiple scales. Our architecture is composed of two main networks: A region proposal network that generates a list of regions of interest (ROIs) and a second corresponds to a network that use these ROIs for classification into face/non-face. Both of them share the full-image convolution features of a pre-trained ResNet-50 model. Experimental study was conducted on the famous WIDER Face and FDDB databases. The obtained results proved the efficiency as well as the feasibility of the proposed method to deal with multi-scale face detection problems.

[1]  Lijun Yin,et al.  Facial Expression Recognition by De-expression Residue Learning , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[2]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[3]  Wei Liu,et al.  SSD: Single Shot MultiBox Detector , 2015, ECCV.

[4]  Kaiming He,et al.  Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

[6]  Kavita Bala,et al.  Inside-Outside Net: Detecting Objects in Context with Skip Pooling and Recurrent Neural Networks , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[7]  Steven C. H. Hoi,et al.  Face Detection using Deep Learning: An Improved Faster RCNN Approach , 2017, Neurocomputing.

[8]  Shuo Yang,et al.  From Facial Parts Responses to Face Detection: A Deep Learning Approach , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[9]  Dumitru Erhan,et al.  Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[10]  Paul A. Viola,et al.  Robust Real-Time Face Detection , 2001, International Journal of Computer Vision.

[11]  Rama Chellappa,et al.  HyperFace: A Deep Multi-Task Learning Framework for Face Detection, Landmark Localization, Pose Estimation, and Gender Recognition , 2019, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[12]  Erik Learned-Miller,et al.  FDDB: A benchmark for face detection in unconstrained settings , 2010 .

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

[14]  Jian Sun,et al.  Joint Cascade Face Detection and Alignment , 2014, ECCV.

[15]  Li-Jia Li,et al.  Multi-view Face Detection Using Deep Convolutional Neural Networks , 2015, ICMR.

[16]  Canqun Yang,et al.  A hybrid deep learning CNN-ELM for age and gender classification , 2018, Neurocomputing.

[17]  Xiaolin Hu,et al.  Joint Training of Cascaded CNN for Face Detection , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[18]  Deva Ramanan,et al.  Face detection, pose estimation, and landmark localization in the wild , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

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

[20]  Marios Savvides,et al.  Towards a deep learning framework for unconstrained face detection , 2016, 2016 IEEE 8th International Conference on Biometrics Theory, Applications and Systems (BTAS).

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

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