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Face feature point detection based on nonlinear high-dimensional space

Author

Listed:
  • Guoyong Wang

    (HuaiHua Normal College)

  • Lokanayaki Karnan

    (St Francis de Sales College)

  • Faez M. Hassan

    (Mustansiriyah University)

Abstract

In the past few years, face recognition is one of the major areas of research. Face recognition has one advantage over the other methods that it does not require direct contact with an individual to verify their identity. This feature is useful for surveillance, tracking, and detection systems. General data collection is a challenge for other biometrics: if the epidermal tissue is damaged in some way, such as bruising or breaking, hand—and finger-based techniques may become useless. Although there are many face recognition algorithms that work well in restricted environments, face recognition is still a difficult problem in practical application. Face feature point detection is obtained in nonlinear high dimensional space. A face recognition algorithm based on nonlinear extraction is proposed. In this algorithm, the nonlinear feature extraction algorithm is introduced into the process of face recognition, the face feature matching threshold is preprocessed, and the simulated genetic annealing algorithm is combined with the deep belief network. Firstly, the simulated genetic annealing algorithm is used to optimize the network connection weight of the deep belief. The preprocessed face feature matching threshold is optimized to enhance the robustness of the traditional algorithm for weather, illumination, morphology, and other external factors. The simulation results show that the algorithm has strong stability in extracting features and can recognize face images effectively, like high precision.

Suggested Citation

  • Guoyong Wang & Lokanayaki Karnan & Faez M. Hassan, 2022. "Face feature point detection based on nonlinear high-dimensional space," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(1), pages 312-321, March.
    • Handle: RePEc:spr:ijsaem:v:13:y:2022:i:1:d:10.1007_s13198-021-01406-2
    DOI: 10.1007/s13198-021-01406-2
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    References listed on IDEAS

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    1. Chirag Sharma & Amandeep Bagga & Bhupesh Kumar Singh & Mohammad Shabaz, 2021. "A Novel Optimized Graph-Based Transform Watermarking Technique to Address Security Issues in Real-Time Application," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-27, April.
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