Research on Face Intelligent Perception Technology Integrating Deep Learning under Different Illumination Intensities

Abstract

Aiming at the problem of face recognition under different illumination intensities combined with deep learning algorithm, this research designs a new loss function, i-center loss function and integrates the structure of migration learning algorithm on the basis of LeNets++ deep learning network. The face image data set labled faces in the wild with different illumination intensities and the image data set of supermarket monitoring system are used to train and test the improved LeNets++ deep learning network based on softmax, center and i-center loss function, and a variety of common image recognition networks. The calculation results show that although the amount of data required for the training of LeNets++ deep learning network is much larger than other networks selected in the study, when the loss function is changed to i-center, the accuracy of face image recognition under different light intensities is significantly improved, reaching 99.65%. In the supermarket data set, the maximum face recognition rate of the algorithm using i-center loss function is 99.07%, which is 0.21% and 0.6% higher than that of using center softmax and softmax loss function, respectively. Therefore, experiments show that the improved deep learning neural network based on i-center loss function can improve the effect of face recognition under different illumination intensities.