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A multi-scale face detection algorithm based on improved SSD model

Published: 17 May 2019 Publication History

Abstract

At present, the face detection model based on single convolutional neural network has the problem of the low accuracy of small-scale face detection when solving the problem of face detection at different scales. So, we propose an improved multi-scale face detection method based on SSD. The method adopts the feature-dense connection strategy to improve the network structure of the basic network in the SSD model, strengthening the information mobility between different convolutional layers and improving the feature description ability of the basic network. Then, the detection accuracy of small-scale faces is improved by introducing context information into shallow features. We evaluate our proposed architecture on WIDER FACE dataset, and it achieves a high average precision (AP) of 73.1%, 90% and 92% for different data sets ("difficult", "medium" and "simple") respectively, which is higher than several other methods.

References

[1]
Z. Zhang, P. Luo, C. C. Loy, et al. Facial Landmark Detection by Deep Multi-task Learning{J}. European Conference on Computer Vision (ECCV), 2014:94--108.
[2]
Y. Sun, X. Wang, X. Tang. Deep convolutional network cascade for facial point detection{C}. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013:3476--3483.
[3]
D. Cristinacce, T. Cootes. Automatic feature localization with constrained local models {J}. Pattern Recognition, 2008, 41(10): 3054--3067.
[4]
Crosswhite N, Byrne J, Stauffer C, et al. Template adaptation for face verification and identification{C}. IEEE International Conference on Automatic Face & Gesture Recognition, Washington, D.C., USA, 2017:1--8.
[5]
Majumdar A, Singh R, Vatsa M. Face verification via class sparsity based supervised encoding{J}. IEEE Transaction on Pattern Analysis and Machine Intelligence, 2017, 39(6): 1273--1280.
[6]
Gao Y, Ma J, Yuille A L. Semi-Supervised Sparse Representation Based Classification for Face Recognition with Insufficient Labeled Samples {J}. IEEE Transactions on Image Processing, 2017, 26(5):2545--2560.
[7]
Wang Y, Wang M, Chen Y, et al. A novel virtual samples-based sparse representation method for face recognition{J}. Optik - International Journal for Light and Electron Optics, 2014, 125(15):3908--3912.
[8]
C.Dong, C.L.Chen, K.He, et al. Learning a deep convolution network for image super-resolution {M}. Computer Vision - ECCV 2014. Springer International Publishing, 2014:184--199.
[9]
Tong T, Li G, et al. Image Super-Resolution Using Dense Skip Connections{C}. IEEE International Conference on Computer Vision (ICCV). 2017: 4809--4817.
[10]
Paul Viola, Michael Jones. Rapid object detection using a boosted cascade of simple features{C}. In Computer Vision and Pattern Recognition, 2001Proceedings of the 2001 IEEE Computer Society Conference on. IEEE, 2001, 1, I--511.
[11]
Hongliang Jin, Qingshan Liu, Hanqing LU, Xiaofeng Tong. Face detection using improved lbp under bayesian framework{C}. In Image and Graphics (ICIG'04), Third International Conference on. IEEE, 2004:306--309.
[12]
Haoxiang Li, Zhe Lin, Xiaohui Shen, Jonathan Brandt, Gang Hua. A convolutional neural network cascade for face detection{C}. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2015:5325--5334.
[13]
Shuo Yang, Ping Luo, Chen-Change Loy, Xiaoou Tang. From facial parts responses to face detection: A deep learning approach{C}. In Proceedings of the IEEE International Conference on Computer Vision (ICCV). 2015:3676--3684.
[14]
Shaoqing Ren, Kaiming He, Ross Girshick. Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks{C}. In Advances in neural information processing systems. 2015: 91--99.
[15]
Huaizu Jiang, Erik Learned-Miller. Face detection with the faster r-cnn{J}. IEEE International Conference on Automatic Face & Gesture Recognition. 2016: 650--657.
[16]
Long J, Shelhamer E, Darrell T. Fully Convolutional Networks for Semantic Segmentation {J}. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2014, 39(4):640--651.
[17]
Dai Jifeng, Li Yi, He Kaiming, et al. R-FCN: Object detection via region-based fully convolutional networks{J}. Computer Science, 2016.
[18]
Liu W, Anguelov D, Erhan D, et al. SSD: Single Shot MultiBox Detector{C}. European Conference on Computer Vision, Amsterdam, Netherlands, 2016:21--37.
[19]
Shifeng Zhang, Xiangyu Zhu, Zhen Lei, et al. S^3FD: Single Shot Scale-Invariant Face Detector{C}. IEEE International Conference on Computer Vision. 2017: 192--201.
[20]
Peiyun Hu, Deva Ramanan. Finding Tiny Faces{C}. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017: 1522--1530.
[21]
Huang G, Liu Z, Laurens V D M, et al. Densely Connected Convolutional Networks{J}. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016.
[22]
Yang S, Ping L, Chen C L, et al. WIDER FACE: A Face Detection Benchmark{C}. Processing of the IEEE Conference on Computer Vision and Pattern Recognition, 2016:5525--5533.
[23]
Zhu C, Zheng Y, Luu K, et al. CMS-RCNN: Contextual Multi-Scale Region-based CNN for Unconstrained Face Detection{J}. 2016.
[24]
Zhang K, Zhang Z, Li Z, et al. Joint Face Detection and Alignment Using Multitask Cascaded Convolutional Networks{J}. IEEE Signal Processing Letters, 2016, 23(10):1499--1503.

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  • (2022)An Efficient Automatic Face Mask Detection System for Human Safety Based on Deep Learning using Novel YOLOv3 in Comparison of YOLO with Improved Accuracy2022 14th International Conference on Mathematics, Actuarial Science, Computer Science and Statistics (MACS)10.1109/MACS56771.2022.10022356(1-5)Online publication date: 12-Nov-2022
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cover image ACM Other conferences
ACM TURC '19: Proceedings of the ACM Turing Celebration Conference - China
May 2019
963 pages
ISBN:9781450371582
DOI:10.1145/3321408
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 17 May 2019

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Author Tags

  1. SSD model
  2. contextual information
  3. feature map fusion
  4. feature-dense connection strategy
  5. multi-scale face detection

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Cited By

View all
  • (2024)A Comprehensive Survey on Backdoor Attacks and their Defenses in Face Recognition SystemsIEEE Access10.1109/ACCESS.2024.3382584(1-1)Online publication date: 2024
  • (2023)A systematic review of object detection from images using deep learningMultimedia Tools and Applications10.1007/s11042-023-15981-y83:4(12253-12338)Online publication date: 24-Jun-2023
  • (2022)An Efficient Automatic Face Mask Detection System for Human Safety Based on Deep Learning using Novel YOLOv3 in Comparison of YOLO with Improved Accuracy2022 14th International Conference on Mathematics, Actuarial Science, Computer Science and Statistics (MACS)10.1109/MACS56771.2022.10022356(1-5)Online publication date: 12-Nov-2022
  • (2022)Generating Masked Facial Datasets Using Dlib-Machine Learning Library2022 4th International Conference on Advanced Science and Engineering (ICOASE)10.1109/ICOASE56293.2022.10075601(66-70)Online publication date: 21-Sep-2022
  • (2020)Oracle Bone Inscriptions Detection in Rubbings Based on Deep Learning2020 IEEE 9th Joint International Information Technology and Artificial Intelligence Conference (ITAIC)10.1109/ITAIC49862.2020.9339132(1671-1674)Online publication date: 11-Dec-2020

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