• SHAIK NAHEEN
• SHAIK JAFAR AMEEN

In this project, we propose a novel method based on deep learning to solve the adverse impact imposed by illumination variation in the face recognition process. Firstly, illumination pre-processing is applied to improve the adverse effects of intense illumination changes on face images. Secondly, the Log-Gabor filter is used to obtain the Log-Gabor feature images of different scales and directions, then, LBP (Local Binary Pattern) features of images subblock is extracted. Lastly, texture feature histograms are formed and input into the deep belief network (DBN) visual layer, then face classification and recognition are completed through deep learning in DBN. Experimental results show that superior performance can be obtained in the developed approach by comparisons with some state-of-the-arts.

Face Recognition, deep learning, Illumination conditions, deep belief network

"DEEP LEARNING FOR FACE RECOGNITION UNDER COMPLEX ILLUMINATION CONDITIONS BASED ON LOG-GABOR AND LBP", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 1, page no.b703-b711, January-2024, Available :http://www.jetir.org/papers/JETIR2401180.pdf

"DEEP LEARNING FOR FACE RECOGNITION UNDER COMPLEX ILLUMINATION CONDITIONS BASED ON LOG-GABOR AND LBP", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 1, page no. ppb703-b711, January-2024, Available at : http://www.jetir.org/papers/JETIR2401180.pdf