Erbir, Muhammed AliÜnver, Halil Murat2025-01-212025-01-2120212193-567Xhttps://doi.org/10.1007/s13369-021-05693-6https://hdl.handle.net/20.500.12587/23600In this study, developments in face recognition are examined. Some methods are presented to increase the accuracy rate in face recognition by using transfer learning with VGGFace2 dataset and 4 different CNN models. While some of these tested offers decreased the accuracy rate, some of them increased. Effects of histogram balancing, expanding the training data, extracting the effect of non-facial portions of images and vertically aligning images on the accuracy rate were determined and compared to the accuracy rates of original images. As the optimal solution, transfer learning from the InceptionV3 model was preferred, vertical positioning was made, and an accuracy rate of 95.47% was achieved when 10% of the images were used for testing and 90% for training in a 100 people subset of VGGFace2 dataset. In LFW, one of the widely used datasets in the literature, an accuracy rate of 100% has been achieved by exceeding the highest accuracy achieved so far and all images in the LFW database have been recognized without any problems. © 2021, King Fahd University of Petroleum & Minerals.eninfo:eu-repo/semantics/closedAccessBiometric verification; Deep learning; Face recognition; Face verification; Image processing; VGGFace2 datasetArticle4698901891110.1007/s13369-021-05693-62-s2.0-85105778610Q1