The development of biometric applications, such as facial recognition (FR), has recently
become important in smart cities. Many scientists and engineers around the world have
focused on establishing increasingly robust and accurate algorithms and methods for these
types of systems and their applications in everyday life. FR is developing technology with
multiple real-time applications. The goal of this paper is to develop a complete FR system
using transfer learning in fog computing and cloud computing. The developed system uses
deep convolutional neural networks (DCNN) because of the dominant representation; there
are some conditions including occlusions, expressions, illuminations, and pose, which can
affect the deep FR performance. DCNN is used to extract relevant facial features. These
features allow us to compare faces between them in an efficient way. The system can be
trained to recognize a set of people and to learn via an online method, by integrating the
new people it processes and improving its predictions on the ones it already has. The proposed
recognition method was tested with different three standard machine learning algorithms
(Decision Tree (DT), K Nearest Neighbor(KNN), Support Vector Machine (SVM)).
The proposed system has been evaluated using three datasets of face images (SDUMLAHMT,
113, and CASIA) via performance metrics of accuracy, precision, sensitivity, specificity,
and time. The experimental results show that the proposed method achieves superiority
over other algorithms according to all parameters. The suggested algorithm results in higher
accuracy (99.06%), higher precision (99.12%), higher recall (99.07%), and higher specificity
(99.10%) than the comparison algorithms.