An Improved Multi-task Based Face Feature Recognition Model
DONG Penjing
LUO Jieyuan
TANG Xin
Abstract：Deep learning is one of the important fields of machine learning.Multi-task learning(MTL)based on convolutional neural networks(CNNs)has achieved great success in the field of computer vision.The key of multi-task learning is to learn the shared representation of multiple tasks when the structure of the model is unchanged,so that the model of multi-task learning has more generalization ability.This algorithm designs an efficient adaptive feature interaction layer.MobileNetV3 and feature interac-tion model is used to allow different task features to adaptively determine the sharing of features between tasks.Then,through the improved multi-task loss function,the loss of different tasks is weighted and balanced,so that the difference in the training efficien-cy of the multi-task training can achieve better results.The experimental results show that the precision of multi-task training is higher than that of single task training,which meets the experimental requirements.
Keywords：multi-tasking learningCNNsfeature interactiveMobileNetV3 network
Publication Date：2025-12-20
Online Publishing Date：2026-03-09(First online date of this platform, not the publication date of the document)
Pages：6( 3524-3529 )