Wang Qingwei, Zhang Xiaolong, Li Xiaofeng
Harbin Huade University, Harbin 150025, China.
Northeast Forestry University, Harbin 150040, China.
Math Biosci Eng. 2022 Feb 10;19(4):3803-3819. doi: 10.3934/mbe.2022175.
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.
为解决不同人体运动条件下面部特征点识别清晰度和识别效率的问题,提出了一种基于遗传神经网络(GNN)算法的面部特征点识别方法。作为技术平台,将使用海思960开发板。采用优化的BP神经网络算法对人体运动面部图像进行采集和分类,并将遗传算法引入神经网络算法中对人体运动面部图像进行训练。结合改进的GNN算法,通过面部特征点的动态移植来检测面部特征点,并将检测到的面部特征点传输到面部对齐算法中以实现面部特征点识别。结果表明,该方法在不同人体运动图像中面部特征点识别的效率和准确率均高于85%,抗噪性能良好,平均召回率约为90%,且耗时较短。表明该方法在人体运动图像识别领域具有一定的参考价值。