An improved transfer learning based siamese network for face recognation

In the digital era, interest in algorithms and theories for face recognition systems FR have been growing rapidly. Criminal identification, video surveillance, unmanned and autonomous vehicles, and building access control, are just examples of real applications that are gaining attraction among industries. FR is currently a highly difficult and complex subject in deep learning DL, neural networks, pattern analysis, and computer vision domains. Different learning groups, including controller environment and uncontrolled environment, have debated this issue. FR is a novel artificial intelligence application that DL has discovered recently. Earlier, many efforts have been dedicated to building accurate and adaptive FR models. However, recognizing faces in unconstrained environments poses a significant challenge due to various factors such as head pose, age, illumination, and facial expression variations, and others. Therefore , the aim of this study is to develop an efficient FR approach based on a Siamese neural network SNNs and Transfer Learning methods TL. The proposed approach employs SNNs with Visual Geometry Group 16 VGG-16 as a background for efficient FR especially in the case of individuals having similar facial features, and for the purpose of precisely identifying individuals in varying environments. The proposed approach consists of several phases, first data gathering, second data pre-processing, third model building, then comparing the proposed VGG-16 with three more convolutional networks (EfficientNet, ResNetB0, and ConvNeXt algorithms) to keep sure that the proposed approach is robust. For this purpose, labeled faces in the wild LFW dataset were used for SNN with VGG-16. After training the networks, the SNNs with VGG-16 exhibited low loss and a high accuracy in FR. Performance of the architectures were measured using K-Fold cross validation for 5 partition. According to results, EfficientNet, RestNet50 and ConvNext produced 77.75% accuracy, 95% and 93.75 % accuracy respectively.On the other hand, SNN with VGG-16 exhibited a low loss and produced the best accuracy in FR with 96.25%.