Face feature point detection based on nonlinear high-dimensional space

Guoyong Wang (), Lokanayaki Karnan () and Faez M. Hassan ()
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Guoyong Wang: HuaiHua Normal College
Lokanayaki Karnan: St Francis de Sales College
Faez M. Hassan: Mustansiriyah University

International Journal of System Assurance Engineering and Management, 2022, vol. 13, issue 1, No 32, 312-321

Abstract: 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.

Keywords: Face recognition; Biometrics; Algorithm; Genetic annealing; Eigenvalue (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s13198-021-01406-2

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