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Mathematical Problems in Engineering
Volume 2017, Article ID 1376726, 9 pages
https://doi.org/10.1155/2017/1376726
Research Article

Feature Extraction and Fusion Using Deep Convolutional Neural Networks for Face Detection

College of Sciences, Northeastern University, Shenyang 110819, China

Correspondence should be addressed to Xiangde Zhang; nc.ude.uen.liam@edgnaixgnahz

Received 12 August 2016; Revised 17 October 2016; Accepted 26 October 2016; Published 24 January 2017

Academic Editor: Wonjun Kim

Copyright © 2017 Xiaojun Lu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Citations to this Article [8 citations]

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  • Siqi Tang, Zhisong Pan, and Xingyu Zhou, “Low-Rank and Sparse Based Deep-Fusion Convolutional Neural Network for Crowd Counting,” Mathematical Problems in Engineering, vol. 2017, pp. 1–11, 2017. View at Publisher · View at Google Scholar
  • Ying Liu, Yanan Peng, Kengpang Lim, and Nam Ling, “A novel image retrieval algorithm based on transfer learning and fusion features,” World Wide Web, vol. 22, no. 3, pp. 1313–1324, 2018. View at Publisher · View at Google Scholar
  • Przemysław Dolata, Mariusz Mrzygłód, and Jacek Reiner, “Double-stream Convolutional Neural Networks for Machine Vision Inspection of Natural Products,” Applied Artificial Intelligence, pp. 1–17, 2018. View at Publisher · View at Google Scholar
  • Yang Wang, Yin Lv, Dali Guo, Shu Zhang, and Shixiang Jiao, “A Novel Multi-Input AlexNet Prediction Model for Oil and Gas Production,” Mathematical Problems in Engineering, vol. 2018, pp. 1–9, 2018. View at Publisher · View at Google Scholar
  • Maher Jridi, Ayman Al Falou, Yassin Kortli, and Mohamed Atri, “A comparative study of CFS, LBP, HOG, SIFT, SURF, and BRIEF techniques for face recognition,” Proceedings of SPIE - The International Society for Optical Engineering, vol. 10649, 2018. View at Publisher · View at Google Scholar
  • Ying Du, Yadong Liu, Qingzhu Shao, Lingen Luo, Jindun Dai, Gehao Sheng, and Xiuchen Jiang, “Single Line-to-Ground Faulted Line Detection of Distribution Systems With Resonant Grounding Based on Feature Fusion Framework,” IEEE Transactions on Power Delivery, vol. 34, no. 4, pp. 1766–1775, 2019. View at Publisher · View at Google Scholar
  • Shyamala Balakumar, Selvaperumal Sundaramoorthy, Ramasubramanian Bhoopalan, and G. Prabhakar, “Detection of Moving Object in Dynamic Visual Sequences Based on Partial Least Squares Classifier,” Journal of Medical Systems, vol. 43, no. 8, 2019. View at Publisher · View at Google Scholar
  • Masud Pervez, Kh Tohidul Islam, Sudanthi Wijewickrema, and Stephen O'Leary, “An Exploration of Deep Transfer Learning for Food Image Classification,” 2018 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2018, 2019. View at Publisher · View at Google Scholar