Open AccessFacial feature point recognition method for human motion image using GNN
Author(s) -
Qingwei Wang,
AUTHOR_ID,
Xiaolong Zhang,
Xiaofeng Li,
AUTHOR_ID,
AUTHOR_ID
Publication year - 2022
Publication title -
mathematical biosciences and engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.451
H-Index - 45
eISSN - 1551-0018
pISSN - 1547-1063
DOI - 10.3934/mbe.2022175
Subject(s) - artificial intelligence , feature (linguistics) , computer science , computer vision , pattern recognition (psychology) , three dimensional face recognition , facial recognition system , artificial neural network , face hallucination , face (sociological concept) , face detection , sociology , social science , philosophy , linguistics
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.