Facial feature point recognition method for human motion image using GNN

Qingwei Wang; Xiaolong Zhang; Xiaofeng Li; Qingwei Wang; Xiaolong Zhang; Xiaofeng Li

doi:10.3934/mbe.2022175

Mathematical Biosciences and Engineering

2022, Volume 19, Issue 4: 3803-3819. doi: 10.3934/mbe.2022175

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Research article Special Issues

Facial feature point recognition method for human motion image using GNN

1.
Harbin Huade University, Harbin 150025, China
2.
Northeast Forestry University, Harbin 150040, China
3.
Department of Information Engineering, Heilongjiang International University, Harbin 150025, China
academic editor:Weizheng Wang

Received: 29 September 2021 Revised: 14 January 2022 Accepted: 24 January 2022 Published: 10 February 2022

To address the problems of facial feature point recognition clarity and recognition efficiency in different human motion conditions, a facial feature point recognition method using Genetic Neural Network (GNN) algorithm was proposed. As the technical platform, weoll be using the Hikey960 development board. The optimized BP neural network algorithm is used to collect and classify human motion facial images, and the genetic algorithm is introduced into neural network algorithm to train human motion facial images. Combined with the improved GNN algorithm, the facial feature points are detected by the dynamic transplantation of facial feature points, and the detected facial feature points are transferred to the face alignment algorithm to realize facial feature point recognition. The results show that the efficiency and accuracy of facial feature point recognition in different human motion images are higher than 85% and the performance of anti-noise is good, the average recall rate is about 90% and the time-consuming is short. It shows that the proposed method has a certain reference value in the field of human motion image recognition.
- Genetic Neural Network,
- human motion,
- facial feature points,
- facial detection,
- BP neural network
Citation: Qingwei Wang, Xiaolong Zhang, Xiaofeng Li. Facial feature point recognition method for human motion image using GNN[J]. Mathematical Biosciences and Engineering, 2022, 19(4): 3803-3819. doi: 10.3934/mbe.2022175

Related Papers:

Abstract

To address the problems of facial feature point recognition clarity and recognition efficiency in different human motion conditions, a facial feature point recognition method using Genetic Neural Network (GNN) algorithm was proposed. As the technical platform, weoll be using the Hikey960 development board. The optimized BP neural network algorithm is used to collect and classify human motion facial images, and the genetic algorithm is introduced into neural network algorithm to train human motion facial images. Combined with the improved GNN algorithm, the facial feature points are detected by the dynamic transplantation of facial feature points, and the detected facial feature points are transferred to the face alignment algorithm to realize facial feature point recognition. The results show that the efficiency and accuracy of facial feature point recognition in different human motion images are higher than 85% and the performance of anti-noise is good, the average recall rate is about 90% and the time-consuming is short. It shows that the proposed method has a certain reference value in the field of human motion image recognition.

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