Research on Face Recognition Technology Fusion Deep Learning Under Different Light Intensity Changes

Abstract

Aiming at the problem of face recognition under different illumination intensities combined with deep learning algorithms, this research designed a new type of loss function, the I-center loss function. Use face image data set LFW with different light intensity to train and test LeNets++ deep learning network based on softmax, center, I-center loss function, and a variety of common image recognition networks. The calculation results show that although the LeNets++ deep learning network training requires much more data than other networks selected in the study, when the loss function is changed to I-center, the network has a significant improvement in the accuracy of face image recognition under different light intensities, reaching 99.65%. Therefore, experiments have proved that the use of an improved deep learning neural network based on the I-center loss function can improve the face recognition effect under different light intensities.