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Research on Face Recognition Method by Autoassociative Memory Based on RNNs

Author

Listed:
  • Qi Han
  • Zhengyang Wu
  • Shiqin Deng
  • Ziqiang Qiao
  • Junjian Huang
  • Junjie Zhou
  • Jin Liu

Abstract

In order to avoid the risk of the biological database being attacked and tampered by hackers, an Autoassociative Memory (AAM) model is proposed in this paper. The model is based on the recurrent neural networks (RNNs) for face recognition, under the condition that the face database is replaced by its model parameters. The stability of the model is proved and analyzed to slack the constraints of AAM model parameters. Besides, a design procedure about solving AAM model parameters is given, and the face recognition method by AAM model is established, which includes image preprocessing, AAM model training, and image recognition. Finally, simulation results on two experiments show the feasibility and performance of the proposed face recognition method.

Suggested Citation

  • Qi Han & Zhengyang Wu & Shiqin Deng & Ziqiang Qiao & Junjian Huang & Junjie Zhou & Jin Liu, 2018. "Research on Face Recognition Method by Autoassociative Memory Based on RNNs," Complexity, Hindawi, vol. 2018, pages 1-12, December.
    • Handle: RePEc:hin:complx:8524825
    DOI: 10.1155/2018/8524825
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    Cited by:

    1. Mengxin Liu & Wenyuan Tao & Xiao Zhang & Yi Chen & Jie Li & Chung-Ming Own, 2019. "GO Loss: A Gaussian Distribution-Based Orthogonal Decomposition Loss for Classification," Complexity, Hindawi, vol. 2019, pages 1-10, December.
    2. Qi Han & Heng Yang & Tengfei Weng & Guorong Chen & Jinyuan Liu & Yuan Tian, 2021. "Multimodal Identification Based on Fingerprint and Face Images via a Hetero-Associative Memory Method," Mathematics, MDPI, vol. 9(22), pages 1-14, November.

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