Deep learning model for person identification and verification on low illumination images

Abstract

Performances of the deep learning models for person identification and verification are degraded on low illumination images due to the generated facial embeddings being unable to be matched with the trained good illumination facial embeddings. Person verification on low illumination images is a challenging task. The existing techniques have adopted an approach of enhancing the low illumination images and performing the person verification in the enhanced images. But these techniques have not achieved satisfactory results, because the gamma value is kept constant in the power law intensity transformation function to enhance the images. To obtain better performance, we propose a deep learning-based framework that consists of a contrast enhancement module, called as contrast enhancement network (CENet); person identification; and person verification modules. The CENet is built based on the residual network, which predicts the gamma value based on the illumination of the input image. The predicted value is used to perform gamma correction on the image to improve the brightness difference between the faces and their background, whereas the existing techniques are keeping the gamma value as constant for image enhancement. After performing the image enhancement, the enhanced image is given as input to the person identification module. Then the detected faces are verified by the person verification module. Experimental results show that the proposed framework has achieved an improvement of 3.4% to 13% in person identification and verification accuracy on the extended yale face dataset to the existing methods.